cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u')","in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def","if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4","'\\n'.join(stripped_before + [''] + stripped_after) # Even though we do auto_up, it can","+ lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and","https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\x1b[1;9A'] = (Keys.Escape, Keys.Up)","whitespace. On third call, restore the original whitespace and cursor position. \"\"\" buffer","tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb from","buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed","= buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position","not in multiline, execute. When in multiline, try to intelligently add a newline","in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: #","new_lines[row] buffer.text = '\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer","'{') def backward_paragraph(event): \"\"\" Move back one paragraph of text \"\"\" text =","start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] +","last element is the # buffer text and cursor position as we last","from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from","multiple blank # lines correctly. # Gives the positions right before one or","cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text,","@r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable =","i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If there is a","this is a special # marker group that will match # the empty","= new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document =","None for m in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) -","event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if","@r.add_binding(Keys.Escape, '<') def beginning(event): \"\"\" Move to the beginning \"\"\" event.current_buffer.cursor_position = 0","= '\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer","TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position): state.clear() if","the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and","the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else:","CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document = buffer.document cursor_line,","those # have changed, reset. The state here is global, but that's fine,","Remove the lines with no prompt if match.group('ps1prompt') is not None: return '\\r'","warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >=","\"\"\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly.","text)).lstrip() lines = text.split('\\r') # Make sure multilines end in two newlines for","buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before","= event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg,","cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position =","= event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace,","2: # Exactly one space at the cursor. Remove it. cursor_position -= 1","p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning =","except subprocess.CalledProcessError as e: print(copy_command[0], \"error:\", e, file=sys.stderr) def system_paste(): if \"Linux\" in","+ stripped_after) # Even though we do auto_up, it can be out of","def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to","'\\n') # Replace tabs with four spaces (C-x C-y will still paste the","= min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break if","if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need","is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \"<C-?>\" ALL_KEYS.append(\"<C-?>\") ANSI_SEQUENCES['\\x1b[ab']","row, col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not","PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at","that will match # the empty string. # Otherwise it will not #","(if any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped)","# position out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position","by indent, except for the first line. It is assumed that the text","from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from","[] for (row, col, msg, m) in warnings: # Handle SyntaxErrorMessage which is","doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\n'):","+= 1 if multiline and inside_string(text, row, col): # We are inside a","if \"Linux\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try:","from # the string). (?P<line>.*)\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match):","line, _x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i],","m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def","line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for","default binding # Some terminals (Like iTerm2) seem to paste \\r\\n line endings","character could be # - or _ for i, c in enumerate(word): if","iTerm2) seem to paste \\r\\n line endings in a # bracketed paste. See:","the original text. The last element is the # buffer text and cursor","released version of prompt-toolkit. ANSI_SEQUENCES['\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape,","for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text)","multiline. \"\"\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state,","is mainly used to trigger autocompletion while typing. \"\"\" # Original text &","outputs. Example: >>> split_prompts(''' ... In [1]: a = 1 ... ... In","for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at","any change to be enough clear the state. The worst that # happens","= split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first","it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer","re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front","Get the indentation for the comment delimiters min_indent = float('inf') for line in","already at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks","(text, cursor_position): state.clear() if len(state) == 0: # Replace all whitespace at the","of state is the original text. The last element is the # buffer","be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 -","at the end will be stripped by the prompt anyway. # This just","= '\\n'.join(lines) # TODO: Set the cursor position correctly n_changed = 2*(cursor_line -","positions or positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions:","previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions","= event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab","deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \"\"\" Insert characters at cursor","True if not positions or positions[0] >= buffer.cursor_position: return p = positions[0] for","@r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \"\"\" Move back to the beginning of the line,","and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape,","for down because down is already at the end of the # prompt.","two newlines for i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If","file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \"error:\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW)","think it matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after","+= '\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's","= matching_parens(text) for opening, closing in matching: if closing.end == (row, col): new_pos","]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \"\"\" Move back to the beginning of the","buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the","' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one","buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer","subprocess import sys import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings()","state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first","from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings","TODO: Remove the lines with no prompt if match.group('ps1prompt') is not None: return","@r.add_binding(Keys.Escape, '>') def end(event): \"\"\" Move to the end \"\"\" event.current_buffer.cursor_position = len(event.current_buffer.text)","elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state =","Handle SyntaxErrorMessage which is the same warning for the whole # line. if","from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings","cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \"\"\" On blank line, delete all surrounding blank","move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text =","= document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before","m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position =","if cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def","+ '# ' + line[min_indent:]) else: lines.append(line) new_text = '\\n'.join(lines) # TODO: Set","= None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state =","to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1)","to be enough clear the state. The worst that # happens here is","save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to escape all spaces here","completion. \"\"\" # Text before cursor on the line must be whitespace because","in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer,","def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row, col","end of the buffer, accept unless we are in a # docstring row,","cursor position as we last left them. If either of those # have","\\r\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\r\\n',","row += 1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] =","Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because down is","len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer","@r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count","= event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before =","stripped from the outputs. Example: >>> split_prompts(''' ... In [1]: a = 1","+ '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\r') # Make sure multilines","the first character could be # - or _ for i, c in","closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell()","with four spaces (C-x C-y will still paste the text exactly) data =","in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position =","stripped_after) # Even though we do auto_up, it can be out of bounds","sure multilines end in two newlines for i, line in enumerate(lines): try: validate_text(line)","as bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape,","event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \"\"\" Right that wraps around in multiline.","is a special # marker group that will match # the empty string.","len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position","session and returns a list of inputs Outputs are stripped. If no prompts","the line must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor","return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text =","# If we are at the end of the buffer, accept unless we","encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\"Error: could not find\", copy_command[0], file=sys.stderr)","cursor position correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line >=","\"\"\" On blank line, delete all surrounding blank lines, leaving just one. On","document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening, closing in matching:","\"<C-/>\" ALL_KEYS.append(\"<C-/>\") ANSI_SEQUENCES['\\x1b\"5/'] = Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is","the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip():","m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p')","# The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\"\"\" repl function for re.sub","else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line, it's already","a syntax error, we can't use the CMD_QUEUE (it # breaks things). lines","if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else:","lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state is","otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if","Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g","re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts","min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break","\\[\\d+\\]:\\ '] ps2_prompts = [r'\\ *\\.\\.\\.:\\ ?', r'\\.\\.\\.\\ ?', '\\N{CLAPPING HANDS SIGN}+\\\\ ?⎢\\\\","delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent","len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer","new_lines[row-1], new_lines[row] buffer.text = '\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event):","= True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position =","filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK,","- m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f')","= cursor_position - m.start(0) break else: pos = buffer.cursor_position if pos: deleted =","multiline, try to intelligently add a newline or execute. \"\"\" buffer = event.current_buffer","terminals (Like iTerm2) seem to paste \\r\\n line endings in a # bracketed","= event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position:","= buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just before","Example: >>> split_prompts(''' ... In [1]: a = 1 ... ... In [2]:","'' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data,","be # - or _ for i, c in enumerate(word): if c.isalnum(): word","the whole # line. if m.col != col: continue pos = document.translate_row_col_to_index(row, col)","auto_newline(event.current_buffer) # Always accept the line if the previous key was Up #","import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import","isolated blank line, delete that one. On nonblank line, delete any immediately following","= buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could be #","we can do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True)","for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position","matching_parens(text) for opening, closing in matching: if opening.start == (row, col): new_pos =","of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position)","cursor_position)) elif len(state) == 2: # Exactly one space at the cursor. Remove","history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer","a # released version of prompt-toolkit. ANSI_SEQUENCES['\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] = (Keys.Escape,","delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def","0: # Replace all whitespace at the cursor (if any) with a single","in line.rstrip(): lines[i] += '\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the","backward_delete_char(event) # Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ')","(C-?) in iTerm2 settings Keys.ControlQuestionmark = \"<C-?>\" ALL_KEYS.append(\"<C-?>\") ANSI_SEQUENCES['\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash =","# Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because down","warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row,","document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1:","because the first character could be # - or _ for i, c","lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's already indented","event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \"\"\" Left that wraps around in multiline. \"\"\" if","1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: #","document.translate_index_to_position(to - 1) end_line += 1 else: start_line = cursor_line end_line = start_line","cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor =","= document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening, closing in","won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark,","'def test():\\n pass'] \"\"\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\n'","if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \"\"\" When tab should","Always accept the line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492.","re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \"\"\" Move back to the beginning","== len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\n'.join(new_lines)","Move back to the beginning of the line, ignoring whitespace. \"\"\" current_line =","the # buffer text and cursor position as we last left them. If","will not # match (because all \\r's # have been stripped from #","# https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False)","seem to paste \\r\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737","matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos","event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position: return p","getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \"\"\" Right that wraps","correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else:","in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else:","because # we consider any change to be enough clear the state. The","in multiline, execute. When in multiline, try to intelligently add a newline or","Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because","rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text","' for i, j in emoji + [emoji_pudb]] + [r'In\\ \\[\\d+\\]:\\ '] ps2_prompts","= text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state is the","event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \"\"\" Move back one","are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept","new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down()","off. # TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position):","state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and cursor position","data = event.data buffer = event.current_buffer # Be sure to use \\n as","front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\r|))? # If the prompt is","# merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo()","import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from","= document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line","c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break","# whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event):","event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key","move_cursor=True): \"\"\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event. This","= event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position =","except FileNotFoundError: print(\"Error: could not find\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0],","insert whitespace, do that instead of completion. \"\"\" # Text before cursor on","buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position:","0 if cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO)","event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event):","Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX,","# Strip prompts off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position)","multilines end in two newlines for i, line in enumerate(lines): try: validate_text(line) except","all whitespace at the cursor (if any) with a single space. state.append((text, cursor_position))","Strip prompts off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row","0 uncomment = (all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line])","in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] #","prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\r|))? # If","keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \"\"\"","# docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text,","lines with no prompt if match.group('ps1prompt') is not None: return '\\r' + match.group('line')","kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos = None","'\\N{CLAPPING HANDS SIGN}+\\\\ ?⎢\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE =",">>> split_prompts(''' ... In [1]: a = 1 ... ... In [2]: a","it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in range(1,","event.app.current_buffer # Avoid issues when text grows or shrinks below, keeping the cursor","be out of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text","if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position","positions: if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position","True if not positions or positions[-1] <= buffer.cursor_position: return p = positions[-1] for","not line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for line in","to the end \"\"\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank","the positions right before one or more blank lines BLANK_LINES = re.compile(r'\\S *(\\n","indented by indent, except for the first line. It is assumed that the","def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m","the line ending, # it should act like insert mode. overwritten_text = otext[ocpos:ocpos","+= event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError,","escape all spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\","False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event):","break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def","event. This is mainly used to trigger autocompletion while typing. \"\"\" # Original","= re.compile(r'\\S *(\\n *\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \"\"\" Move forward one paragraph","The resulting text is indented by indent, except for the first line. It","prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData","inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a","+ blank_lines_after == 1: new_text = '\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after","pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if","Text before cursor on the line must be whitespace because of the #","The state here is global, but that's fine, because # we consider any","document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent","multiline_enter(event): \"\"\" When not in multiline, execute. When in multiline, try to intelligently","insert_text_ovewrite(buffer, data, move_cursor=True): \"\"\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert`","= event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position","filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and","blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\n'.join(stripped_before + [''] +","with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text","= p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a","+= 1 else: start_line = cursor_line end_line = start_line + 1 # Get","state. The worst that # happens here is that we resume when we","lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \"\"\" When not in multiline, execute. When","The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\"\"\" repl function for re.sub for","and ((len(pks) == 1 and pks[0].key == \"up\") or (len(pks) == 2 and","import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji,","or positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos in reversed(positions): if","= event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos","def undo(event): event.current_buffer.undo() # Need to escape all spaces here because of verbose","m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position =","buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event):","a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and","prompts. \"\"\" # TODO: Remove the lines with no prompt if match.group('ps1prompt') is","delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to","document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines): if start_line","event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) -","not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor:","in multiline. \"\"\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if","- indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces =","cursor. Remove it. cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position))","copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python","len(state) == 3: # Restore original text and cursor position text, cursor_position =","`on_text_insert` event. This is mainly used to trigger autocompletion while typing. \"\"\" #",".mypython import CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure to","prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from","and pks[0].key == \"up\") or (len(pks) == 2 and pks[0].key == \"escape\" and","a list of inputs Outputs are stripped. If no prompts are found the","prompt anyway. # This just makes this function easier to test. lines =","(C-x C-y will still paste the text exactly) data = data.replace('\\t', ' ')","if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\n'): # If we are","we are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1","file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \"error:\", e, file=sys.stderr) def system_paste(): if \"Linux\"","re.sub for clearing prompts Replaces PS1 prompts with \\r and removes PS2 prompts.","inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re","break if min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip() or","event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not","position correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line >= end_line","@r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if","start_line = cursor_line end_line = start_line + 1 # Get the indentation for","@r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text","if cursor_position < 0: cursor_position = 0 if cursor_position > len(text): cursor_position =","min_indent == 0: break if min_indent == float('inf'): min_indent = 0 uncomment =","start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1","contains no carriage returns (\\r). Trailing whitespace and newlines is stripped from the","in multiline, try to intelligently add a newline or execute. \"\"\" buffer =","= buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def","This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version","['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event):","# released version of prompt-toolkit. ANSI_SEQUENCES['\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] = (Keys.Escape, Keys.Down)","import validate_text text = textwrap.dedent(text).strip() + '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines =","for i, c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() +","+ stripped_after) elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text","wraps around in multiline. \"\"\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position +=","elif not multiline: # Always accept a single valid line. Also occurs for","is a syntax error, we can't use the CMD_QUEUE (it # breaks things).","iTerm2 settings Keys.ControlQuestionmark = \"<C-?>\" ALL_KEYS.append(\"<C-?>\") ANSI_SEQUENCES['\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \"<C-/>\" ALL_KEYS.append(\"<C-/>\")","m.start(0) break else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def","= True buffer.history_backward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event):","@r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() #","@r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \"\"\" Based on emacs's cycle-spacing On first call,","the string). (?P<line>.*)\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\"\"\" repl","LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent","insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer","buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just before the","as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens","all whitespace. On third call, restore the original whitespace and cursor position. \"\"\"","spaces%-4) else: backward_delete_char(event) # Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape,",".mypython import validate_text text = textwrap.dedent(text).strip() + '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines","indent the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))","auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a single valid line.","accept the line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. #","event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event)","SyntaxErrorMessage which is the same warning for the whole # line. if m.col","word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document","inputs Outputs are stripped. If no prompts are found the text is left","!= '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False)","@r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \"\"\" Move back one paragraph of text","position. :param fire_event: Fire `on_text_insert` event. This is mainly used to trigger autocompletion","assumed that the text contains no carriage returns (\\r). Trailing whitespace and newlines","# If there is a syntax error, we can't use the CMD_QUEUE (it","same warning for the whole # line. if m.col != col: continue pos","sure to use \\n as line ending. # This part is the same","# Don't indent the first line, it's already indented lambda line, _x=[]: bool(_x","pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return","list of inputs Outputs are stripped. If no prompts are found the text","correctly. # Gives the positions right before one or more blank lines BLANK_LINES","word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event):","lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the end of the","\"\"\" Move to the end \"\"\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat","tabs with four spaces (C-x C-y will still paste the text exactly) data","= KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions =","matched, this is a special # marker group that will match # the","buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state","= buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line,","if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This","buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer =","0)) new_text = '\\n'.join(stripped_before + [''] + stripped_after) # Even though we do","True buffer.history_backward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer","-= event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \"\"\"","emacs's cycle-spacing On first call, remove all whitespace (if any) from around the","buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not","is not # matched, this is a special # marker group that will","current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines =","buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position +=","= start_line + 1 # Get the indentation for the comment delimiters min_indent","isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ',","blank lines BLANK_LINES = re.compile(r'\\S *(\\n *\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \"\"\" Move","PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent","will still paste the text exactly) data = data.replace('\\t', ' ') # Strip","re import subprocess import sys import textwrap import platform def get_key_bindings(): # Based","original whitespace and cursor position. \"\"\" buffer = event.app.current_buffer # Avoid issues when","= pos event.current_buffer.cursor_position = p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857","the first line. It is assumed that the text contains no carriage returns","set to S-Enter in iTerm2 settings Keys.ShiftEnter = \"<Shift-Enter>\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\x1b[ag'] = Keys.ShiftEnter","if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False):","= len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)')","but I don't # think it matters here. if (not blank_lines_before and blank_lines_after)","buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count)","# breaks things). lines = ['\\n'.join(lines)] break if '\\n' in line.rstrip(): lines[i] +=","+= 1 matching, mismatching = matching_parens(text) for opening, closing in matching: if closing.end","text_before_cursor = text[:cursor_position] # The first element of state is the original text.","closing in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position","= len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position","row == 0: buffer.cursor_down() row += 1 if row == len(new_lines) - 1:","event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state","of the file, but I don't # think it matters here. if (not","of those # have changed, reset. The state here is global, but that's","except for the first line. It is assumed that the text contains no","= 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state:","buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position =","grows or shrinks below, keeping the cursor # position out of sync cursor_position","text = textwrap.dedent(text).strip() + '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\r') #","of text \"\"\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))):","word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer","\"\"\" # Text before cursor on the line must be whitespace because of","# Get the indentation for the comment delimiters min_indent = float('inf') for line","n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text","In [2]: a ... Out[2]: 1 ... ... In [3]: def test(): ...","!= '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \"\"\" When tab should insert whitespace,","for the first line. It is assumed that the text contains no carriage","-= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX,","= event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \"Linux\" in platform.platform(): copy_command = ['xclip', '-selection',","Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event. This is mainly","isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape,","'3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def","buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer =","do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] #","immediately following blank lines. \"\"\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current =","+ '\\n' return '' def split_prompts(text, indent=''): r\"\"\" Takes text copied from mypython,","cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) ==","line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines =","cursor and replace it with a single space. On second call, remove all","_x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]:","a ... Out[2]: 1 ... ... In [3]: def test(): ... ...: pass","cycle-spacing On first call, remove all whitespace (if any) from around the cursor","pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return","is not None: return '\\r' + match.group('line') + '\\n' elif match.group('ps2prompt') is not","or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = []","positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning =","\"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def","False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if","[3]: def test(): ... ...: pass ... ...: ... ''') ['a = 1',","def backward_word(event): \"\"\" Move back one paragraph of text \"\"\" text = event.current_buffer.text","+ [re.escape(i) + r'\\[\\d+\\]:\\ ' for i, j in emoji + [emoji_pudb]] +","in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) - cursor_position break if","for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in","cursor position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position =","kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \"Linux\" in platform.platform(): copy_command =","CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document =","insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data +","multiline. \"\"\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state,","indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or","= len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. # Gives","1', 'a', 'def test():\\n pass'] \"\"\" from .mypython import validate_text text = textwrap.dedent(text).strip()","If there is a syntax error, we can't use the CMD_QUEUE (it #","restore the original whitespace and cursor position. \"\"\" buffer = event.app.current_buffer # Avoid","float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if","lines = ['\\n'.join(lines)] break if '\\n' in line.rstrip(): lines[i] += '\\n' lines[0] =","pass'] \"\"\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\n' text =","word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event):","event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None","if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer","= buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The","event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?)","BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,","= LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if","Keys.ControlQuestionmark Keys.ControlSlash = \"<C-/>\" ALL_KEYS.append(\"<C-/>\") ANSI_SEQUENCES['\\x1b\"5/'] = Keys.ControlSlash # This won't work until","event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text","split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line,","from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import","event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text =","text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) <","key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for","if match.group('ps1prompt') is not None: return '\\r' + match.group('line') + '\\n' elif match.group('ps2prompt')","+ line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text =","indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for","def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer = event.current_buffer #","= otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):]","def backward_paragraph(event): \"\"\" Move back one paragraph of text \"\"\" text = event.current_buffer.text","# Handle SyntaxErrorMessage which is the same warning for the whole # line.","textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\r') # Make sure multilines end in two newlines","= data.replace('\\r', '\\n') # Replace tabs with four spaces (C-x C-y will still","import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection,","buffer.cursor_position # Don't overwrite the newline itself. Just before the line ending, #","test(): ... ...: pass ... ...: ... ''') ['a = 1', 'a', 'def","from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys,","= 0 @r.add_binding(Keys.Escape, '>') def end(event): \"\"\" Move to the end \"\"\" event.current_buffer.cursor_position","in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0","lines correctly. # Gives the positions right before one or more blank lines","= matching_parens(text) for opening, closing in matching: if opening.start == (row, col): new_pos","@r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection()","text in lines[1:]: # TODO: Send last chunk as bracketed paste, so it","and cursor position as we last left them. If either of those #","load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import","if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC,","buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp)","(all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines","1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if row ==","= float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line)","this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\"Error:","spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\ '] +","at the end of the buffer, accept unless we are in a #","if '\\n' in line.rstrip(): lines[i] += '\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't","3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True)","exactly # as they did where we left off. # TODO: Use event.previous_key_sequence","change to be enough clear the state. The worst that # happens here","is the original text. The last element is the # buffer text and","r\"\"\" Takes text copied from mypython, Python, or IPython session and returns a",">= end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text","cursor_position: pos = m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted)","(it # breaks things). lines = ['\\n'.join(lines)] break if '\\n' in line.rstrip(): lines[i]","def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX,","= m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize()","\"\"\" # Original text & cursor position. otext = buffer.text ocpos = buffer.cursor_position","bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';')","... In [2]: a ... Out[2]: 1 ... ... In [3]: def test():","be enough clear the state. The worst that # happens here is that","Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row,","+ len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer =","def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow =","@r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape,","word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could be","Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row,","event.current_buffer.cursor_left() # M-[ a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter","= (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next =","If either of those # have changed, reset. The state here is global,","= True if not positions or positions[0] >= buffer.cursor_position: return p = positions[0]","# of the line. (?P<noprompt>(?(prompt)\\r|))? # If the prompt is not # matched,","< buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False):","buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position: return p =","a parallel for down because down is already at the end of the","# Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def","= buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer","min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] == '#' for line","custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings","buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position","return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document =","None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \"\"\" Based on emacs's cycle-spacing On first","pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,","line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't","buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer =","first call, remove all whitespace (if any) from around the cursor and replace","truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\n'): # If we","= state[0] state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position >","M-[ a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter = \"<Shift-Enter>\"","@r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to S-Enter","\"\"\" When not in multiline, execute. When in multiline, try to intelligently add","blank_lines_after += 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before","WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,","line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in","pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \"\"\" Insert characters at","# Always accept a single valid line. Also occurs for unclosed single #","# isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace('","and cursor position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position","= event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0)","end \"\"\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines","if state and state[-1] != (text, cursor_position): state.clear() if len(state) == 0: #","same as the default binding # Some terminals (Like iTerm2) seem to paste","buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state","(accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from","state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space at the cursor.","bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX,","= text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of","buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer =","act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] +","makes this function easier to test. lines = [i.rstrip() for i in lines]","= rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original","prompt_toolkit.key_binding.bindings.named_commands import (accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import","event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text","lines = text.split('\\r') # Make sure multilines end in two newlines for i,","event.current_buffer document = buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row +=","for opening, closing in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1,","def cycle_spacing(event): \"\"\" Based on emacs's cycle-spacing On first call, remove all whitespace","below ps1_prompts = [r'>>>\\ '] + [re.escape(i) + r'\\[\\d+\\]:\\ ' for i, j","for the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not","= (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer","cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position:","the file, but I don't # think it matters here. if (not blank_lines_before","toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h')","returns (\\r). Trailing whitespace and newlines is stripped from the outputs. Example: >>>","len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position =","n_changed = 2*(cursor_line - start_line + 1) if cursor_line >= end_line - 1:","len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if","the text exactly) data = data.replace('\\t', ' ') # Strip prompts off pasted","col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer =","new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row =","0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text","def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for","get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(),","PS2 prompts. \"\"\" # TODO: Remove the lines with no prompt if match.group('ps1prompt')","the beginning \"\"\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \"\"\" Move to","True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0","resulting text is indented by indent, except for the first line. It is","blank line, delete all surrounding blank lines, leaving just one. On isolated blank","not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer","overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos +","that wraps around in multiline. \"\"\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position","lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be stripped","in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text)","\"\"\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0)","cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline","nonblank line, delete any immediately following blank lines. \"\"\" buffer = event.app.current_buffer document","and removes PS2 prompts. \"\"\" # TODO: Remove the lines with no prompt","load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def",".processors import get_pyflakes_warnings import re import subprocess import sys import textwrap import platform","[re.escape(i) + r'\\[\\d+\\]:\\ ' for i, j in emoji + [emoji_pudb]] + [r'In\\","prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding","filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU,","= buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('')","import re import subprocess import sys import textwrap import platform def get_key_bindings(): #","M-[ a b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark =","buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching","not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] #","from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings","delete_blank_lines(event): \"\"\" On blank line, delete all surrounding blank lines, leaving just one.","# M-[ a b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark","part is the same as the default binding # Some terminals (Like iTerm2)","transpose_lines(event): buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:]","around in multiline. \"\"\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg","... ...: ... ''') ['a = 1', 'a', 'def test():\\n pass'] \"\"\" from","pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This can","uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line)","I don't # think it matters here. if (not blank_lines_before and blank_lines_after) or","cursor_position)) elif len(state) == 3: # Restore original text and cursor position text,","'\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's already","@r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1]))","line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for line in lines_after_current:","of text \"\"\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text):","-= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state:","# happens here is that we resume when we shouldn't if things look","= textwrap.dedent(text).strip() + '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\r') # Make","buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if","= event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position =","= document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line,","= (all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip())","pos event.current_buffer.cursor_position = p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is","and state[-1] != (text, cursor_position): state.clear() if len(state) == 0: # Replace all","trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def","elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def","space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped +","any immediately following blank lines. \"\"\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current","if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the","event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def","positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions: if pos","buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next =","(\\r). Trailing whitespace and newlines is stripped from the outputs. Example: >>> split_prompts('''","paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\r\\n', '\\n') data = data.replace('\\r', '\\n') # Replace","r'\\[\\d+\\]:\\ ' for i, j in emoji + [emoji_pudb]] + [r'In\\ \\[\\d+\\]:\\ ']","be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer","# TODO: Set the cursor position correctly n_changed = 2*(cursor_line - start_line +","When in multiline, try to intelligently add a newline or execute. \"\"\" buffer","= m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \"\"\" Move back","This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3':","run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\"Error: could not","False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need","backward_word(event): \"\"\" Move back one paragraph of text \"\"\" text = event.current_buffer.text cursor_position","import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse","= Keys.ControlQuestionmark Keys.ControlSlash = \"<C-/>\" ALL_KEYS.append(\"<C-/>\") ANSI_SEQUENCES['\\x1b\"5/'] = Keys.ControlSlash # This won't work","event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \"\"\" Insert characters at cursor position. :param fire_event:","When not in multiline, execute. When in multiline, try to intelligently add a","else: lines.append(line) new_text = '\\n'.join(lines) # TODO: Set the cursor position correctly n_changed","if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent ==","= buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line","to trigger autocompletion while typing. \"\"\" # Original text & cursor position. otext","buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))):","= custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals))","bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: #","in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] =","prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r =","is the same warning for the whole # line. if m.col != col:","before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1)","= '\\n'.join(stripped_before + [''] + stripped_after) # Even though we do auto_up, it","position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself.","['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python 3.6 we","= word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text)","overwrite the newline itself. Just before the line ending, # it should act","== float('inf'): min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] == '#'","else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after","lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and cursor","blank lines, leaving just one. On isolated blank line, delete that one. On","new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection","*\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \"\"\" Move forward one paragraph of text \"\"\"","== 2 and pks[0].key == \"escape\" and isinstance(pks[1].key, str) and pks[1].key in ['p',","lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data","if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event):","print(copy_command[0], \"error:\", e, file=sys.stderr) def system_paste(): if \"Linux\" in platform.platform(): paste_command = ['xsel',","function easier to test. lines = [i.rstrip() for i in lines] return lines","'\\r' + match.group('line') + '\\n' elif match.group('ps2prompt') is not None: return match.group('line') +","= event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor)","cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The","PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose","comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue","SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app","event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. #","of completion. \"\"\" # Text before cursor on the line must be whitespace","text contains no carriage returns (\\r). Trailing whitespace and newlines is stripped from","= [r'\\ *\\.\\.\\.:\\ ?', r'\\.\\.\\.\\ ?', '\\N{CLAPPING HANDS SIGN}+\\\\ ?⎢\\\\ ?'] PS1_PROMPTS_RE =","# In Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p","end_line = start_line + 1 # Get the indentation for the comment delimiters","backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import","call, remove all whitespace (if any) from around the cursor and replace it","0: break if min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip()","text[:cursor_position] # The first element of state is the original text. The last","whitespace and cursor position. \"\"\" buffer = event.app.current_buffer # Avoid issues when text","\"escape\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else:","+ blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\n'.join(stripped_before + ['']","Replaces PS1 prompts with \\r and removes PS2 prompts. \"\"\" # TODO: Remove","prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS","is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \"\"\" When not in","word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position =","[r'>>>\\ '] + [re.escape(i) + r'\\[\\d+\\]:\\ ' for i, j in emoji +","else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position","the lines with no prompt if match.group('ps1prompt') is not None: return '\\r' +","with no prompt if match.group('ps1prompt') is not None: return '\\r' + match.group('line') +","ANSI_SEQUENCES['\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace)","paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6","< end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' +","position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position = 0","buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not","buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position +=","1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) #","the cursor (if any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor)","custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions","fire_event: Fire `on_text_insert` event. This is mainly used to trigger autocompletion while typing.","matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1:","newlines is stripped from the outputs. Example: >>> split_prompts(''' ... In [1]: a","as we last left them. If either of those # have changed, reset.","SyntaxError: # If there is a syntax error, we can't use the CMD_QUEUE","CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure to use \\n","row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent","col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent =","Restore original text and cursor position text, cursor_position = state[0] state.clear() if cursor_position","document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1 else: start_line =","def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos =","get_pyflakes_warnings import re import subprocess import sys import textwrap import platform def get_key_bindings():","buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position:","cycle_spacing(event): \"\"\" Based on emacs's cycle-spacing On first call, remove all whitespace (if","self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs","warnings: # Handle SyntaxErrorMessage which is the same warning for the whole #","treat multiple blank # lines correctly. # Gives the positions right before one","Fire `on_text_insert` event. This is mainly used to trigger autocompletion while typing. \"\"\"","= len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else:","= rstripped + ' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2:","is not None: return match.group('line') + '\\n' return '' def split_prompts(text, indent=''): r\"\"\"","' ') # Strip prompts off pasted text document = buffer.document row, col","cursor position. \"\"\" buffer = event.app.current_buffer # Avoid issues when text grows or","@r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0:","# Extraneous newlines at the end will be stripped by the prompt anyway.","for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent:","1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row]","Trailing whitespace and newlines is stripped from the outputs. Example: >>> split_prompts(''' ...","the same warning for the whole # line. if m.col != col: continue","buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text","binding # Some terminals (Like iTerm2) seem to paste \\r\\n line endings in","i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# '","The last element is the # buffer text and cursor position as we","... ... In [2]: a ... Out[2]: 1 ... ... In [3]: def","not None: return match.group('line') + '\\n' return '' def split_prompts(text, indent=''): r\"\"\" Takes","(Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True","\"\"\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0)","+ word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event):","# buffer text and cursor position as we last left them. If either","== 2: # Exactly one space at the cursor. Remove it. cursor_position -=","= textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be stripped by","@r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg,","insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer","if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position","\"\"\" buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor","sys import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([","document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer","forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0)","found the text is left alone. The resulting text is indented by indent,","m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else: pos = buffer.cursor_position","= new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row","<= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This can be","the state. The worst that # happens here is that we resume when","text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) >","from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection","or positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions: if","for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position","call, restore the original whitespace and cursor position. \"\"\" buffer = event.app.current_buffer #","& cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the","' + line[min_indent:]) else: lines.append(line) new_text = '\\n'.join(lines) # TODO: Set the cursor","try: # In Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8',","@r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape,","TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?')","len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \"\"\" On blank line, delete","settings Keys.ShiftEnter = \"<Shift-Enter>\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0]","BLANK_LINES = re.compile(r'\\S *(\\n *\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \"\"\" Move forward one","the prompt is not # matched, this is a special # marker group","[2]: a ... Out[2]: 1 ... ... In [3]: def test(): ... ...:","are at the end of the buffer, accept unless we are in a","last chunk as bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE:","use the CMD_QUEUE (it # breaks things). lines = ['\\n'.join(lines)] break if '\\n'","event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer","inside_string(text, row, col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif","if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{')","the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\r|))? # If the prompt","changed, reset. The state here is global, but that's fine, because # we","len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position =","Replace tabs with four spaces (C-x C-y will still paste the text exactly)","is indented by indent, except for the first line. It is assumed that","Extraneous newlines at the end will be stripped by the prompt anyway. #","= len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \"\"\" Move back one paragraph of text","load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document","cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position","element of state is the original text. The last element is the #","# Always accept the line if the previous key was Up # Requires","back to the beginning of the line, ignoring whitespace. \"\"\" current_line = event.app.current_buffer.document.current_line","cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position =","min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent =","line.rstrip(): lines[i] += '\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first","or _ for i, c in enumerate(word): if c.isalnum(): word = word[:i] +","first element of state is the original text. The last element is the","prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \"\"\" When tab should insert","def multiline_enter(event): \"\"\" When not in multiline, execute. When in multiline, try to","@r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow","getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count","Original text & cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't","of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks","in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer,","ignoring whitespace. \"\"\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if","prompts with \\r and removes PS2 prompts. \"\"\" # TODO: Remove the lines","import subprocess import sys import textwrap import platform def get_key_bindings(): # Based on","import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys","the outputs. Example: >>> split_prompts(''' ... In [1]: a = 1 ... ...","WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position","def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event):","here is that we resume when we shouldn't if things look exactly #","emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess import sys import","' + lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space","at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\r|))? # If the","in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end","*(\\n *\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \"\"\" Move forward one paragraph of text","newline itself. Just before the line ending, # it should act like insert","not line.strip(): blank_lines_after += 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current","try to intelligently add a newline or execute. \"\"\" buffer = event.current_buffer document","line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent","or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't","shrinks below, keeping the cursor # position out of sync cursor_position = buffer.cursor_position","= event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if pos > cursor_position:","line, delete all surrounding blank lines, leaving just one. On isolated blank line,","'-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6 we can do","history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \"\"\" Based","or blank_lines_before + blank_lines_after == 1: new_text = '\\n'.join(stripped_before + stripped_after) elif blank_lines_before","= document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor)","first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines","prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline,","]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings =","- event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state =","event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event):","pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first",">= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape,","@r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text","not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def","break if '\\n' in line.rstrip(): lines[i] += '\\n' lines[0] = textwrap.indent(lines[0], indent, #","= None @r.add_binding(Keys.Right) def right_multiline(event): \"\"\" Right that wraps around in multiline. \"\"\"","https://github.com/ipython/ipython/issues/9737 data = data.replace('\\r\\n', '\\n') data = data.replace('\\r', '\\n') # Replace tabs with","the end of the file, but I don't # think it matters here.","copy_command = ['pbcopy'] try: # In Python 3.6 we can do this: #","'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def","+= 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1)","buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position","@r.add_binding(Keys.Left) def left_multiline(event): \"\"\" Left that wraps around in multiline. \"\"\" if event.current_buffer.cursor_position","BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) +","respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\n'): #","the beginning of the line, ignoring whitespace. \"\"\" current_line = event.app.current_buffer.document.current_line before_cursor =","ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab,","Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))?","trigger autocompletion while typing. \"\"\" # Original text & cursor position. otext =","event.arg if getattr(event.current_buffer.selection_state, \"shift_arrow\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \"\"\" Right","blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1","copied from mypython, Python, or IPython session and returns a list of inputs","# - or _ for i, c in enumerate(word): if c.isalnum(): word =","use \\n as line ending. # This part is the same as the","e: print(copy_command[0], \"error:\", e, file=sys.stderr) def system_paste(): if \"Linux\" in platform.platform(): paste_command =","merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash,","indent(event): \"\"\" When tab should insert whitespace, do that instead of completion. \"\"\"","lines = [] for i, line in enumerate(document.lines): if start_line <= i <","1) end_line += 1 else: start_line = cursor_line end_line = start_line + 1","buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def","col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event):","that wraps around in multiline. \"\"\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position","\"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor:","for opening, closing in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1,","\"\"\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl,","warning for the whole # line. if m.col != col: continue pos =","document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0:","buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer","function for re.sub for clearing prompts Replaces PS1 prompts with \\r and removes","data.replace('\\r\\n', '\\n') data = data.replace('\\r', '\\n') # Replace tabs with four spaces (C-x","If no prompts are found the text is left alone. The resulting text","off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1","... In [3]: def test(): ... ...: pass ... ...: ... ''') ['a","do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError:","third call, restore the original whitespace and cursor position. \"\"\" buffer = event.app.current_buffer","if things look exactly # as they did where we left off. #","\"\"\" # TODO: Remove the lines with no prompt if match.group('ps1prompt') is not","# In Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True)","and cursor position. \"\"\" buffer = event.app.current_buffer # Avoid issues when text grows","been stripped from # the string). (?P<line>.*)\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE))","endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\r\\n', '\\n') data","all spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\ ']","future:\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?) in iTerm2","Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text):","should act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos]","- 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \"shift_arrow\", False): buffer.selection_state =","spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4)","Don't indent the first line, it's already indented lambda line, _x=[]: bool(_x or","the end of the buffer, accept unless we are in a # docstring","positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or","we resume when we shouldn't if things look exactly # as they did","second call, remove all whitespace. On third call, restore the original whitespace and","_x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send","except SyntaxError: # If there is a syntax error, we can't use the","text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\r') # Make sure multilines end in","c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def","syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if the previous","(?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line.","else: break blank_lines_after = 0 for line in lines_after_current: if not line.strip(): blank_lines_after","event.data buffer = event.current_buffer # Be sure to use \\n as line ending.","= [r'>>>\\ '] + [re.escape(i) + r'\\[\\d+\\]:\\ ' for i, j in emoji","WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower())","def system_paste(): if \"Linux\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command =","instead. if state and state[-1] != (text, cursor_position): state.clear() if len(state) == 0:","= Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \"\"\" When not in multiline,","lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the","iTerm2 settings Keys.ShiftEnter = \"<Shift-Enter>\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\x1bOM'] = Keys.ShiftEnter if","IPython session and returns a list of inputs Outputs are stripped. If no","if len(state) == 0: # Replace all whitespace at the cursor (if any)","not multiline: # Always accept a single valid line. Also occurs for unclosed","In Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p =","the newline itself. Just before the line ending, # it should act like","\"up\") or (len(pks) == 2 and pks[0].key == \"escape\" and isinstance(pks[1].key, str) and","beginning of the line, ignoring whitespace. \"\"\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor"],"string":"[\n \"text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) <\",\n \"event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future\",\n \"if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text)\",\n \"import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace,\",\n \"the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent =\",\n \"i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the\",\n \"accept_line(event) elif not multiline: # Always accept a single valid line. Also occurs\",\n \"or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at the end\",\n \"= event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos = None for m\",\n \"pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p\",\n \"cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\"\",\n \"mismatching = matching_parens(text) for opening, closing in matching: if opening.start == (row, col):\",\n \"= re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match\",\n \"# Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) #\",\n \"continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent =\",\n \"= get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg, m) in warnings:\",\n \"line. if m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions\",\n \"def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow =\",\n \"cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def\",\n \"event.current_buffer.cursor_position = p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in\",\n \"r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text,\",\n \"matching, mismatching = matching_parens(text) for opening, closing in matching: if closing.end == (row,\",\n \"len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On\",\n \"= '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1,\",\n \"= Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if\",\n \"len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position =\",\n \"cursor_position < 0: cursor_position = 0 if cursor_position > len(text): cursor_position = len(text)\",\n \"in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1)\",\n \"buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer =\",\n \"space at the cursor. Remove it. cursor_position -= 1 text = rstripped +\",\n \"from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from\",\n \"just one. On isolated blank line, delete that one. On nonblank line, delete\",\n \"event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0\",\n \"3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE,\",\n \"cursor_position - m.start(0) break else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos)\",\n \"The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state:\",\n \"= 0 if min_indent == 0: break if min_indent == float('inf'): min_indent =\",\n \"\\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <=\",\n \"state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position > len(text): cursor_position\",\n \"It is assumed that the text contains no carriage returns (\\\\r). Trailing whitespace\",\n \"if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position\",\n \"buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position\",\n \"# TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^\",\n \"buffer = event.app.current_buffer # Avoid issues when text grows or shrinks below, keeping\",\n \"new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft)\",\n \"do that instead of completion. \\\"\\\"\\\" # Text before cursor on the line\",\n \"encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0],\",\n \"document = buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1\",\n \"+ len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor:\",\n \"event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position =\",\n \"Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for\",\n \"in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent =\",\n \"load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings,\",\n \"import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import\",\n \"stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as\",\n \"upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in\",\n \"import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr\",\n \"when we shouldn't if things look exactly # as they did where we\",\n \"need a parallel for down because down is already at the end of\",\n \"it should act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text =\",\n \"e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else:\",\n \"< 0: cursor_position = 0 if cursor_position > len(text): cursor_position = len(text) return\",\n \"auto_up, it can be out of bounds from trailing # whitespace buffer.cursor_position =\",\n \"\\\"\\\"\\\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event. This is\",\n \"blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before +\",\n \"def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command\",\n \"line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines =\",\n \"def forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row, col\",\n \"'\\\\n' elif match.group('ps2prompt') is not None: return match.group('line') + '\\\\n' return '' def\",\n \"@r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text\",\n \"== 0: buffer.cursor_down() row += 1 if row == len(new_lines) - 1: new_lines.append('')\",\n \"= True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count =\",\n \"buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not\",\n \"https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because down is already\",\n \"previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel\",\n \"will match # the empty string. # Otherwise it will not # match\",\n \"def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks)\",\n \"m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position =\",\n \"buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around\",\n \"'\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0))\",\n \"but that's fine, because # we consider any change to be enough clear\",\n \"text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding\",\n \"# Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front |\",\n \"= data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces\",\n \"cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_)\",\n \"buffer.cursor_position: return p = positions[0] for pos in positions: if pos >= buffer.cursor_position:\",\n \"+ [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS\",\n \"if m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape,\",\n \"[data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send last chunk as bracketed\",\n \"(len(pks) == 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in\",\n \"# The first element of state is the original text. The last element\",\n \"Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a\",\n \"back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"or (len(pks) == 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key\",\n \"@r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in\",\n \"event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word\",\n \"split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python, or IPython session and\",\n \"buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The default\",\n \"for i, j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts =\",\n \"blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif\",\n \"= event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if\",\n \"instead of completion. \\\"\\\"\\\" # Text before cursor on the line must be\",\n \"= \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash\",\n \"typing. \\\"\\\"\\\" # Original text & cursor position. otext = buffer.text ocpos =\",\n \"end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't\",\n \"(?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for\",\n \"event.current_buffer.undo() # Need to escape all spaces here because of verbose (x) option\",\n \"len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document =\",\n \"down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event):\",\n \"event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position): state.clear() if len(state) ==\",\n \"pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use\",\n \"current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line, it's\",\n \"event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event):\",\n \"= event.current_buffer document = buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row\",\n \"= document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text, row, col): # We\",\n \"are stripped. If no prompts are found the text is left alone. The\",\n \"find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste():\",\n \"lambda line, _x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i] =\",\n \"for unclosed single # quoted strings (which will give a syntax error) accept_line(event)\",\n \"= m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position\",\n \"prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event):\",\n \"for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else: break blank_lines_after\",\n \"return match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied\",\n \"= True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor:\",\n \"= warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0]\",\n \"= document.text_before_cursor text = buffer.text # isspace doesn't respect vacuous truth if (not\",\n \"p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr)\",\n \"or execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor\",\n \"matching, mismatching = matching_parens(text) for opening, closing in matching: if opening.start == (row,\",\n \"the cursor and replace it with a single space. On second call, remove\",\n \"col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline:\",\n \"buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer =\",\n \"event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def\",\n \"carriage returns (\\\\r). Trailing whitespace and newlines is stripped from the outputs. Example:\",\n \"event.current_buffer # Be sure to use \\\\n as line ending. # This part\",\n \"save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo()\",\n \"1 else: start_line = cursor_line end_line = start_line + 1 # Get the\",\n \"up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown)\",\n \"(row, col, msg, m) in warnings: # Handle SyntaxErrorMessage which is the same\",\n \"check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e:\",\n \"and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? #\",\n \"from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings\",\n \"as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if\",\n \"# Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. # Gives the positions\",\n \"style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter,\",\n \"buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for\",\n \"prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from\",\n \"in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text)\",\n \"blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX:\",\n \"# Replace tabs with four spaces (C-x C-y will still paste the text\",\n \"if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b')\",\n \"buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer\",\n \"= warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1]\",\n \"r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert whitespace, do that\",\n \"buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position\",\n \"ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard\",\n \"not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return\",\n \"buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]):\",\n \"= SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any,\",\n \"syntax error, we can't use the CMD_QUEUE (it # breaks things). lines =\",\n \"return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer\",\n \"-= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace():\",\n \"def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of the line, ignoring whitespace.\",\n \"pos in reversed(positions): if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position =\",\n \"delete that one. On nonblank line, delete any immediately following blank lines. \\\"\\\"\\\"\",\n \"# Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not\",\n \"marker group that will match # the empty string. # Otherwise it will\",\n \"text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if\",\n \"edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document\",\n \"event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return\",\n \"in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0\",\n \"indent, except for the first line. It is assumed that the text contains\",\n \"ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in\",\n \"m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move\",\n \"\\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:]\",\n \"m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def\",\n \"use word.capitalize() because the first character could be # - or _ for\",\n \"buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n')\",\n \"= len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position\",\n \"consider any change to be enough clear the state. The worst that #\",\n \"line, delete any immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document =\",\n \"can't use the CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)] break if\",\n \"lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text\",\n \"i, c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower()\",\n \"[r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts))\",\n \"(row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def\",\n \"run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\",\",\n \"= event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position: return\",\n \"def upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m\",\n \"# https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape,\",\n \"= event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position +=\",\n \"_x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent)\",\n \"state[-1] != (text, cursor_position): state.clear() if len(state) == 0: # Replace all whitespace\",\n \"the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the history search\",\n \"pos in positions: if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning =\",\n \"';') def comment(event): buffer = event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position)\",\n \"space. On second call, remove all whitespace. On third call, restore the original\",\n \"@r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True\",\n \"if cursor_line >= end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position -=\",\n \"= event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to\",\n \"e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() #\",\n \"Even though we do auto_up, it can be out of bounds from trailing\",\n \"select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True\",\n \"'>') def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) #\",\n \"print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_,\",\n \"[i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import\",\n \"from .mypython import CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure\",\n \"event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When\",\n \"__version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string,\",\n \"= event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1\",\n \"filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert whitespace, do that instead of\",\n \"to paste \\\\r\\\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data\",\n \"pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,\",\n \"...: pass ... ...: ... ''') ['a = 1', 'a', 'def test():\\\\n pass']\",\n \"event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\"\",\n \"> len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\"\",\n \"below, keeping the cursor # position out of sync cursor_position = buffer.cursor_position buffer.cursor_position\",\n \"option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i,\",\n \"2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P',\",\n \"otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape,\",\n \"shouldn't if things look exactly # as they did where we left off.\",\n \"itself. Just before the line ending, # it should act like insert mode.\",\n \"+ event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state =\",\n \"subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform():\",\n \"= event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection()\",\n \"document.text_before_cursor text = buffer.text # isspace doesn't respect vacuous truth if (not text_after_cursor\",\n \"> len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position\",\n \"is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES\",\n \"# it should act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text\",\n \"Right that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text):\",\n \"to test. lines = [i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def\",\n \"= Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def\",\n \"On isolated blank line, delete that one. On nonblank line, delete any immediately\",\n \"don't treat multiple blank # lines correctly. # Gives the positions right before\",\n \"+ 1 # Get the indentation for the comment delimiters min_indent = float('inf')\",\n \"# think it matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before +\",\n \"= event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event):\",\n \"(row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB)\",\n \"just makes this function easier to test. lines = [i.rstrip() for i in\",\n \"= m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position\",\n \"ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't\",\n \"replace it with a single space. On second call, remove all whitespace. On\",\n \"Always accept a single valid line. Also occurs for unclosed single # quoted\",\n \"get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When in\",\n \"def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b\",\n \"if multiline and inside_string(text, row, col): # We are inside a docstring auto_newline(event.current_buffer)\",\n \"lines = [i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from\",\n \"n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE)\",\n \"buffer text and cursor position as we last left them. If either of\",\n \"buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if\",\n \"1 matching, mismatching = matching_parens(text) for opening, closing in matching: if closing.end ==\",\n \"= event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and\",\n \"def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not\",\n \"\\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\",\",\n \"if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position =\",\n \"= positions[-1] for pos in reversed(positions): if pos <= buffer.cursor_position: break p =\",\n \"check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except\",\n \"# Be sure to use \\\\n as line ending. # This part is\",\n \"is stripped from the outputs. Example: >>> split_prompts(''' ... In [1]: a =\",\n \"\\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when text grows or shrinks below,\",\n \"def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position]\",\n \"verbose (x) option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ '\",\n \"here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text\",\n \"spaces (C-x C-y will still paste the text exactly) data = data.replace('\\\\t', '\",\n \"[''] + stripped_after) # Even though we do auto_up, it can be out\",\n \"len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def\",\n \"not positions or positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos in\",\n \"for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position - m.start(0)\",\n \"indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer\",\n \"None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable\",\n \"new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff\",\n \"cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col =\",\n \"# Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))?\",\n \"= event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm')\",\n \"text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's\",\n \"> cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event):\",\n \"and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key == \\\"up\\\") or\",\n \"buffer = event.current_buffer document = buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position)\",\n \"buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col):\",\n \"= buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text =\",\n \"event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return\",\n \"= LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event):\",\n \"buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position: return p =\",\n \"bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) #\",\n \"the indentation for the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]:\",\n \"@r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) #\",\n \"line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line)\",\n \"'3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert whitespace, do\",\n \"wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -=\",\n \"if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer\",\n \"row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row\",\n \"event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def\",\n \"whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent:\",\n \"= document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down() row\",\n \"text = rstripped + ' ' + lstripped state.append((text, cursor_position)) elif len(state) ==\",\n \"pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1\",\n \"mismatching = matching_parens(text) for opening, closing in matching: if closing.end == (row, col):\",\n \"error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if the previous key\",\n \"return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings\",\n \"m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape,\",\n \"if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import\",\n \"event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to =\",\n \"can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of\",\n \"# prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1],\",\n \"@r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position\",\n \"ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__\",\n \"= event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines)\",\n \"cursor_line >= end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed\",\n \"= buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching,\",\n \"= ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0],\",\n \"Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute.\",\n \"of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE =\",\n \"system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command =\",\n \"import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from\",\n \"reset. The state here is global, but that's fine, because # we consider\",\n \"in warnings: # Handle SyntaxErrorMessage which is the same warning for the whole\",\n \"event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of\",\n \"row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text =\",\n \"\\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b']\",\n \"was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down\",\n \"b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\")\",\n \"'*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\"\",\n \"== '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i,\",\n \"ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is #\",\n \"-= 1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3:\",\n \"insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer\",\n \"@r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When in multiline,\",\n \"'m') def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of the line, ignoring\",\n \"in reversed(positions): if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p\",\n \"input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\",\",\n \"== 0: break if min_indent == float('inf'): min_indent = 0 uncomment = (all(not\",\n \"def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding blank lines, leaving just\",\n \"as they did where we left off. # TODO: Use event.previous_key_sequence instead. if\",\n \"the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for\",\n \"file, but I don't # think it matters here. if (not blank_lines_before and\",\n \"a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if the\",\n \"positions = [] for (row, col, msg, m) in warnings: # Handle SyntaxErrorMessage\",\n \"... ''') ['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import\",\n \"end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text =\",\n \"# bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n')\",\n \"whitespace, do that instead of completion. \\\"\\\"\\\" # Text before cursor on the\",\n \"= None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state:\",\n \"easier to test. lines = [i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste)\",\n \"buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position\",\n \"accept a single valid line. Also occurs for unclosed single # quoted strings\",\n \"@r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0\",\n \"'-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python 3.6 we can\",\n \"a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>')\",\n \"# We don't need a parallel for down because down is already at\",\n \"self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text):\",\n \"not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer\",\n \"= None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row\",\n \"['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent,\",\n \"@r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow\",\n \"+= event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row\",\n \"break else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer,\",\n \"import __version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import\",\n \"= indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data,\",\n \"that instead of completion. \\\"\\\"\\\" # Text before cursor on the line must\",\n \"line in lines_after_current: if not line.strip(): blank_lines_after += 1 else: break if not\",\n \"are found the text is left alone. The resulting text is indented by\",\n \"# Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings,\",\n \"j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\\",\n \"the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\"\",\n \"remove all whitespace (if any) from around the cursor and replace it with\",\n \"one space at the cursor. Remove it. cursor_position -= 1 text = rstripped\",\n \"lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line,\",\n \"import sys import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return\",\n \"# Gives the positions right before one or more blank lines BLANK_LINES =\",\n \"True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg\",\n \"row += 1 if multiline and inside_string(text, row, col): # We are inside\",\n \"len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back\",\n \"= document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if\",\n \"any) from around the cursor and replace it with a single space. On\",\n \"from mypython, Python, or IPython session and returns a list of inputs Outputs\",\n \"buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor\",\n \"Need to escape all spaces here because of verbose (x) option below ps1_prompts\",\n \"state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped + '\",\n \"at the cursor (if any) with a single space. state.append((text, cursor_position)) cursor_position -=\",\n \"'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True)\",\n \"+ r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\\",\n \"event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) -\",\n \"we can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except\",\n \"enumerate(document.lines): if start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else:\",\n \"def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection())\",\n \"and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up',\",\n \"select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace,\",\n \"buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count\",\n \"= document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text # isspace\",\n \"we left off. # TODO: Use event.previous_key_sequence instead. if state and state[-1] !=\",\n \"line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing prompts\",\n \"event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop\",\n \"0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False):\",\n \"textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be stripped by the\",\n \"prompts Replaces PS1 prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO:\",\n \"event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else:\",\n \"def test(): ... ...: pass ... ...: ... ''') ['a = 1', 'a',\",\n \"the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank #\",\n \"import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess import sys\",\n \"Move forward one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\"\",\n \"def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY)\",\n \"g is set to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag']\",\n \"fine, because # we consider any change to be enough clear the state.\",\n \"auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1:\",\n \"select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True\",\n \"else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) #\",\n \"positions[0] for pos in positions: if pos >= buffer.cursor_position: break p = pos\",\n \"have changed, reset. The state here is global, but that's fine, because #\",\n \"lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current =\",\n \"text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not\",\n \"... ... In [3]: def test(): ... ...: pass ... ...: ... ''')\",\n \"blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after ==\",\n \"pass ... ...: ... ''') ['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\"\",\n \"return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning\",\n \"prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize\",\n \"the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a\",\n \"do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor\",\n \"first character could be # - or _ for i, c in enumerate(word):\",\n \"special # marker group that will match # the empty string. # Otherwise\",\n \"buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def\",\n \"@r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text\",\n \"event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row >\",\n \"= ['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6 we\",\n \"delete any immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document\",\n \"On blank line, delete all surrounding blank lines, leaving just one. On isolated\",\n \"0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position =\",\n \"e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection:\",\n \"doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state =\",\n \"event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def\",\n \"\\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try: #\",\n \"(not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before\",\n \"if m.end(0) > cursor_position: pos = m.end(0) - cursor_position break if pos: deleted\",\n \"ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged.\",\n \"a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text =\",\n \"text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) >\",\n \"left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg\",\n \"False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg\",\n \"1) if cursor_line >= end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position\",\n \"state here is global, but that's fine, because # we consider any change\",\n \"col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip())\",\n \"= [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send last chunk as\",\n \"elif len(state) == 3: # Restore original text and cursor position text, cursor_position\",\n \"p = pos event.current_buffer.cursor_position = p # This can be removed once #\",\n \"def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph\",\n \"+ c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF)\",\n \"1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None\",\n \"(if any) from around the cursor and replace it with a single space.\",\n \"remove all whitespace. On third call, restore the original whitespace and cursor position.\",\n \"from .tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors import\",\n \"still paste the text exactly) data = data.replace('\\\\t', ' ') # Strip prompts\",\n \"in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position =\",\n \"buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped =\",\n \"ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer =\",\n \"matching_parens from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import\",\n \"def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text\",\n \"== 3: # Restore original text and cursor position text, cursor_position = state[0]\",\n \"of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text\",\n \"if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key ==\",\n \"buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor:\",\n \"= m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace)\",\n \"any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1\",\n \"that one. On nonblank line, delete any immediately following blank lines. \\\"\\\"\\\" buffer\",\n \"not None: return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None:\",\n \"0: buffer.cursor_down() row += 1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row],\",\n \"# The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if\",\n \"single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped\",\n \"text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text # isspace doesn't respect\",\n \"data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event.\",\n \"and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event)\",\n \"no prompts are found the text is left alone. The resulting text is\",\n \"check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e:\",\n \"data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def\",\n \"'] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?']\",\n \"line ending, # it should act like insert mode. overwritten_text = otext[ocpos:ocpos +\",\n \"buffer.text # isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and\",\n \"event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor\",\n \"<= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '#\",\n \"+ 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right)\",\n \"not # match (because all \\\\r's # have been stripped from # the\",\n \"multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text #\",\n \"except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def\",\n \"in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\"\",\n \"line ending. # This part is the same as the default binding #\",\n \"In Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command,\",\n \"- cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event):\",\n \"beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if\",\n \"event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position()\",\n \"at the end of the file, but I don't # think it matters\",\n \"text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at the end of the buffer,\",\n \"= None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting')\",\n \"match # the empty string. # Otherwise it will not # match (because\",\n \"test. lines = [i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event):\",\n \"line. It is assumed that the text contains no carriage returns (\\\\r). Trailing\",\n \"+ data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l')\",\n \"Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" When tab should insert whitespace, do that instead of completion. \\\"\\\"\\\" #\",\n \"\\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash #\",\n \"or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO:\",\n \"event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat)\",\n \"!= (text, cursor_position): state.clear() if len(state) == 0: # Replace all whitespace at\",\n \"pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key == \\\"up\\\")\",\n \"for pos in reversed(positions): if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position\",\n \"of the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent\",\n \"len(state) == 0: # Replace all whitespace at the cursor (if any) with\",\n \"to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move\",\n \"buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state,\",\n \"reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else: pos\",\n \"paste \\\\r\\\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data =\",\n \"else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a\",\n \"text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state\",\n \"line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n')\",\n \"if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position\",\n \"# have been stripped from # the string). (?P<line>.*)\\\\n # The actual line.\",\n \"cursor on the line must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor\",\n \"buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert\",\n \"auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to\",\n \"positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos in reversed(positions): if pos\",\n \"things look exactly # as they did where we left off. # TODO:\",\n \"@r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer = event.current_buffer\",\n \"match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from\",\n \"False) and ((len(pks) == 1 and pks[0].key == \\\"up\\\") or (len(pks) == 2\",\n \"cursor # position out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text,\",\n \"if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that\",\n \"buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state\",\n \"['pbcopy'] try: # In Python 3.6 we can do this: # run(copy_command, input=text,\",\n \"repl function for re.sub for clearing prompts Replaces PS1 prompts with \\\\r and\",\n \"Exactly one space at the cursor. Remove it. cursor_position -= 1 text =\",\n \"None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\" if\",\n \"string. # Otherwise it will not # match (because all \\\\r's # have\",\n \"def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(),\",\n \"verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of\",\n \"| (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not\",\n \"from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme\",\n \"filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data =\",\n \"= 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped =\",\n \"reversed(positions): if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p #\",\n \">= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right)\",\n \"either of those # have changed, reset. The state here is global, but\",\n \"r\\\"\\\"\\\" repl function for re.sub for clearing prompts Replaces PS1 prompts with \\\\r\",\n \"= 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text =\",\n \"Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"(auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb\",\n \"> cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character\",\n \"cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped + ' '\",\n \"# Restore original text and cursor position text, cursor_position = state[0] state.clear() if\",\n \"else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines =\",\n \"@r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching =\",\n \"merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings =\",\n \"KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = []\",\n \"In [1]: a = 1 ... ... In [2]: a ... Out[2]: 1\",\n \"re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of\",\n \"document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col\",\n \"lines_after_current: if not line.strip(): blank_lines_after += 1 else: break if not blank_lines_before: stripped_before\",\n \"m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position =\",\n \".multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import\",\n \"prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition,\",\n \"== 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after)\",\n \"Based on emacs's cycle-spacing On first call, remove all whitespace (if any) from\",\n \"previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down)\",\n \"m) in warnings: # Handle SyntaxErrorMessage which is the same warning for the\",\n \"document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for\",\n \"text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if\",\n \"< cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event):\",\n \"could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr)\",\n \"bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') #\",\n \"execute. When in multiline, try to intelligently add a newline or execute. \\\"\\\"\\\"\",\n \"C-y will still paste the text exactly) data = data.replace('\\\\t', ' ') #\",\n \"if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:]\",\n \"the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence\",\n \"end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if\",\n \"cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD\",\n \"prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application\",\n \"len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event)\",\n \"quoted strings (which will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always\",\n \"buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def\",\n \"data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs with four\",\n \"to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the history\",\n \"one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m\",\n \"opening, closing in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1])\",\n \"from around the cursor and replace it with a single space. On second\",\n \"\\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position =\",\n \"closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer\",\n \"= min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer\",\n \"event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg\",\n \"keeps a newline at the end of the file, but I don't #\",\n \"\\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to\",\n \"m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape,\",\n \"event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return\",\n \"whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4))\",\n \"get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg, m) in warnings: #\",\n \"@r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document = buffer.document cursor_line, cursor_col =\",\n \"because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE\",\n \"in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In\",\n \"not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event):\",\n \"a = 1 ... ... In [2]: a ... Out[2]: 1 ... ...\",\n \"text. The last element is the # buffer text and cursor position as\",\n \"return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text =\",\n \"buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param\",\n \"@r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row = document.cursor_position_row\",\n \"it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data]\",\n \"there is a syntax error, we can't use the CMD_QUEUE (it # breaks\",\n \"def right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position +\",\n \"def backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m\",\n \"test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text\",\n \"in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data =\",\n \"here is global, but that's fine, because # we consider any change to\",\n \"event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c']\",\n \"beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the\",\n \"https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def\",\n \"if not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace)\",\n \"None: return match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text\",\n \"def left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position -\",\n \"Some terminals (Like iTerm2) seem to paste \\\\r\\\\n line endings in a #\",\n \"# Need to escape all spaces here because of verbose (x) option below\",\n \"document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer =\",\n \"we do auto_up, it can be out of bounds from trailing # whitespace\",\n \"'l') def downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return\",\n \"event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position: return p\",\n \"first line. It is assumed that the text contains no carriage returns (\\\\r).\",\n \"following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1]\",\n \"''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines): if start_line <= i\",\n \"On third call, restore the original whitespace and cursor position. \\\"\\\"\\\" buffer =\",\n \"match.group('ps1prompt') is not None: return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is\",\n \"(Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg,\",\n \"= len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text\",\n \"ending. # This part is the same as the default binding # Some\",\n \"= event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position: return\",\n \"LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer\",\n \"@r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a\",\n \"stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the end\",\n \"add a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline\",\n \"accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) ==\",\n \"Outputs are stripped. If no prompts are found the text is left alone.\",\n \"== (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape,\",\n \"one or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def\",\n \"Otherwise it will not # match (because all \\\\r's # have been stripped\",\n \"'#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line\",\n \"paste_command = ['pbpaste'] try: # In Python 3.6 we can do this: #\",\n \"unclosed single # quoted strings (which will give a syntax error) accept_line(event) else:\",\n \"line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else: break blank_lines_after =\",\n \"re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern})\",\n \"in lines_after_current: if not line.strip(): blank_lines_after += 1 else: break if not blank_lines_before:\",\n \"= otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data)\",\n \"positions or positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos in reversed(positions):\",\n \"= cursor_line end_line = start_line + 1 # Get the indentation for the\",\n \"import (accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings\",\n \"event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos =\",\n \"= event.app.current_buffer # Avoid issues when text grows or shrinks below, keeping the\",\n \"m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return\",\n \"down because down is already at the end of the # prompt. @r.add_binding(Keys.Enter,\",\n \"''') ['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text\",\n \"def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to S-Enter in\",\n \"back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of the line, ignoring whitespace. \\\"\\\"\\\"\",\n \"return p = positions[-1] for pos in reversed(positions): if pos <= buffer.cursor_position: break\",\n \"all surrounding blank lines, leaving just one. On isolated blank line, delete that\",\n \"a single valid line. Also occurs for unclosed single # quoted strings (which\",\n \"= 1 ... ... In [2]: a ... Out[2]: 1 ... ... In\",\n \"break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after =\",\n \">= buffer.cursor_position: return p = positions[0] for pos in positions: if pos >=\",\n \"the text is left alone. The resulting text is indented by indent, except\",\n \"m in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos]\",\n \"- len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move\",\n \"= buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if\",\n \"paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in\",\n \"buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col\",\n \"= buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text = buffer.text\",\n \"next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions\",\n \"original text. The last element is the # buffer text and cursor position\",\n \"not # matched, this is a special # marker group that will match\",\n \"break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer =\",\n \"+ spaces%-4) else: backward_delete_char(event) # Reset the history search text buffer.history_search_text = None\",\n \"1 if multiline and inside_string(text, row, col): # We are inside a docstring\",\n \"the empty string. # Otherwise it will not # match (because all \\\\r's\",\n \"') # Strip prompts off pasted text document = buffer.document row, col =\",\n \"stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline\",\n \"@r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True\",\n \"for m in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) - cursor_position\",\n \"textwrap.indent(lines[0], indent, # Don't indent the first line, it's already indented lambda line,\",\n \"if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1\",\n \"Don't overwrite the newline itself. Just before the line ending, # it should\",\n \"@r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert whitespace, do that instead\",\n \"ending, # it should act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)]\",\n \"print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e,\",\n \"mypython, Python, or IPython session and returns a list of inputs Outputs are\",\n \"up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer\",\n \"cursor_position = buffer.cursor_position pos = None for m in WORD.finditer(text): if m.end(0) >\",\n \"things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0]\",\n \"buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not\",\n \"indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent)\",\n \"line.strip(): blank_lines_after += 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current else:\",\n \"Takes text copied from mypython, Python, or IPython session and returns a list\",\n \"a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a single\",\n \"= event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True\",\n \"Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] !=\",\n \"document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent,\",\n \"event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting')\",\n \"if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event):\",\n \"col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text, row, col): #\",\n \"document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not\",\n \"- start_line + 1) if cursor_line >= end_line - 1: n_changed -= 2\",\n \"'\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if\",\n \"blank_lines_before += 1 else: break blank_lines_after = 0 for line in lines_after_current: if\",\n \"if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer =\",\n \"backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row, col =\",\n \"buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't\",\n \"WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't\",\n \"This just makes this function easier to test. lines = [i.rstrip() for i\",\n \"col, msg, m) in warnings: # Handle SyntaxErrorMessage which is the same warning\",\n \"to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM']\",\n \"['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6 we can\",\n \"document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that\",\n \".tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings\",\n \"p = positions[0] for pos in positions: if pos >= buffer.cursor_position: break p\",\n \"load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import\",\n \"for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position\",\n \"return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph\",\n \"all whitespace (if any) from around the cursor and replace it with a\",\n \"if not line.strip(): blank_lines_after += 1 else: break if not blank_lines_before: stripped_before =\",\n \"event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer\",\n \"0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) #\",\n \"0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection()\",\n \"is global, but that's fine, because # we consider any change to be\",\n \"# Even though we do auto_up, it can be out of bounds from\",\n \"In [3]: def test(): ... ...: pass ... ...: ... ''') ['a =\",\n \"0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip()\",\n \"Be sure to use \\\\n as line ending. # This part is the\",\n \"def auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count)\",\n \"def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is\",\n \"for (row, col, msg, m) in warnings: # Handle SyntaxErrorMessage which is the\",\n \"if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1)))\",\n \"load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document =\",\n \"# matched, this is a special # marker group that will match #\",\n \"1 ... ... In [2]: a ... Out[2]: 1 ... ... In [3]:\",\n \"\\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position =\",\n \"else: auto_newline(event.current_buffer) # Always accept the line if the previous key was Up\",\n \"pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])):\",\n \"e: False) def undo(event): event.current_buffer.undo() # Need to escape all spaces here because\",\n \"uncomment = (all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and\",\n \"def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow =\",\n \"a single space. On second call, remove all whitespace. On third call, restore\",\n \"one. On nonblank line, delete any immediately following blank lines. \\\"\\\"\\\" buffer =\",\n \"rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element\",\n \"downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in\",\n \"= buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position,\",\n \"the CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in\",\n \"event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos = None for m in\",\n \"load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings()\",\n \"document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text # isspace doesn't respect vacuous truth\",\n \"< cursor_position: pos = cursor_position - m.start(0) break else: pos = buffer.cursor_position if\",\n \"# Replace all whitespace at the cursor (if any) with a single space.\",\n \"import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation\",\n \"'\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces (C-x C-y\",\n \"@r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position\",\n \"'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip() +\",\n \"new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event):\",\n \"else: start_line = cursor_line end_line = start_line + 1 # Get the indentation\",\n \"for pos in positions: if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning\",\n \"cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position =\",\n \"indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0\",\n \"lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for\",\n \"single valid line. Also occurs for unclosed single # quoted strings (which will\",\n \"more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\"\",\n \"len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text =\",\n \"at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks =\",\n \"unless we are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row +=\",\n \"\\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\",\",\n \"3: # Restore original text and cursor position text, cursor_position = state[0] state.clear()\",\n \"[textwrap.indent(data, current_line_indent, # Don't indent the first line, it's already indented lambda line,\",\n \"len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape,\",\n \"Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until #\",\n \"right before one or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape,\",\n \"buffer.cursor_position: return p = positions[-1] for pos in reversed(positions): if pos <= buffer.cursor_position:\",\n \"= None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On\",\n \"new_text = '\\\\n'.join(lines) # TODO: Set the cursor position correctly n_changed = 2*(cursor_line\",\n \"- len(rstripped) -1 text = rstripped + ' ' + lstripped state.append((text, cursor_position))\",\n \"_x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines\",\n \"that's fine, because # we consider any change to be enough clear the\",\n \"event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection()\",\n \"if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy']\",\n \"buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped\",\n \"prompt is not # matched, this is a special # marker group that\",\n \"@r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable)\",\n \"are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if\",\n \"file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range()\",\n \"buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position\",\n \"line must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text('\",\n \"elif match.group('ps2prompt') is not None: return match.group('line') + '\\\\n' return '' def split_prompts(text,\",\n \"= 2*(cursor_line - start_line + 1) if cursor_line >= end_line - 1: n_changed\",\n \"None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text))\",\n \"if not line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for line\",\n \"single space. On second call, remove all whitespace. On third call, restore the\",\n \"\\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try:\",\n \"event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event):\",\n \"down is already at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def\",\n \"multiline: # Always accept a single valid line. Also occurs for unclosed single\",\n \"the cursor position correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line\",\n \"'] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji + [emoji_pudb]]\",\n \"= [textwrap.indent(data, current_line_indent, # Don't indent the first line, it's already indented lambda\",\n \"to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark\",\n \"'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) # Even\",\n \"and replace it with a single space. On second call, remove all whitespace.\",\n \"If we are at the end of the buffer, accept unless we are\",\n \"= run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to\",\n \"buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not\",\n \"buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down)\",\n \"= True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer\",\n \"they did where we left off. # TODO: Use event.previous_key_sequence instead. if state\",\n \"(Like iTerm2) seem to paste \\\\r\\\\n line endings in a # bracketed paste.\",\n \"could be # - or _ for i, c in enumerate(word): if c.isalnum():\",\n \"exactly) data = data.replace('\\\\t', ' ') # Strip prompts off pasted text document\",\n \"otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer\",\n \"prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from\",\n \"execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor =\",\n \"+= 1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1],\",\n \"!= col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def\",\n \"1 # Get the indentation for the comment delimiters min_indent = float('inf') for\",\n \"event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?) in iTerm2 settings\",\n \"did where we left off. # TODO: Use event.previous_key_sequence instead. if state and\",\n \"if m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else: pos =\",\n \"= lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always\",\n \"# Exactly one space at the cursor. Remove it. cursor_position -= 1 text\",\n \"1 matching, mismatching = matching_parens(text) for opening, closing in matching: if opening.start ==\",\n \"@r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection())\",\n \"if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent\",\n \"== 1 and pks[0].key == \\\"up\\\") or (len(pks) == 2 and pks[0].key ==\",\n \"*)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of\",\n \"do auto_up, it can be out of bounds from trailing # whitespace buffer.cursor_position\",\n \"reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape,\",\n \"sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def\",\n \"select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True\",\n \"1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else:\",\n \"_ for i, c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize()\",\n \"is left alone. The resulting text is indented by indent, except for the\",\n \"= positions[0] for pos in positions: if pos >= buffer.cursor_position: break p =\",\n \"text copied from mypython, Python, or IPython session and returns a list of\",\n \"should insert whitespace, do that instead of completion. \\\"\\\"\\\" # Text before cursor\",\n \"string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function\",\n \"indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines\",\n \"prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next',\",\n \"end_col = document.translate_index_to_position(to - 1) end_line += 1 else: start_line = cursor_line end_line\",\n \"event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable)\",\n \"document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text # isspace doesn't\",\n \"blank # lines correctly. # Gives the positions right before one or more\",\n \"= subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except\",\n \"buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer =\",\n \"cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer\",\n \"@r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event)\",\n \"for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE\",\n \"= pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions\",\n \"matching_parens(text) for opening, closing in matching: if closing.end == (row, col): new_pos =\",\n \"of the buffer, accept unless we are in a # docstring row, col\",\n \"0: cursor_position = 0 if cursor_position > len(text): cursor_position = len(text) return text,\",\n \"stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the history search text buffer.history_search_text\",\n \"= textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure multilines end in two\",\n \"lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]:\",\n \"import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(),\",\n \"= buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row,\",\n \"[1]: a = 1 ... ... In [2]: a ... Out[2]: 1 ...\",\n \"from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from\",\n \"# TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position): state.clear()\",\n \"LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines\",\n \"TODO: Send last chunk as bracketed paste, so it can be edited CMD_QUEUE.append(text)\",\n \"= \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3':\",\n \"text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at the end of\",\n \"Avoid issues when text grows or shrinks below, keeping the cursor # position\",\n \"line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not # matched, this is a\",\n \"> cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left)\",\n \"0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position =\",\n \"line, delete that one. On nonblank line, delete any immediately following blank lines.\",\n \"if min_indent == 0: break if min_indent == float('inf'): min_indent = 0 uncomment\",\n \"system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste']\",\n \"@r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the\",\n \"# If the prompt is not # matched, this is a special #\",\n \"event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event):\",\n \"LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to\",\n \"= 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position\",\n \"The first element of state is the original text. The last element is\",\n \"On second call, remove all whitespace. On third call, restore the original whitespace\",\n \"down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly\",\n \"# Avoid issues when text grows or shrinks below, keeping the cursor #\",\n \"== \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event)\",\n \"-= len(text_before_cursor) - len(rstripped) -1 text = rstripped + ' ' + lstripped\",\n \"def auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count -\",\n \"lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO:\",\n \"can do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except\",\n \"= 0 if cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX,\",\n \"buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos = None for\",\n \"'' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python, or IPython\",\n \"FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\",\",\n \"@r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\" text\",\n \"set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] =\",\n \"'d') def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos\",\n \"\\\\r's # have been stripped from # the string). (?P<line>.*)\\\\n # The actual\",\n \"None: return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None: return\",\n \"'# ' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the\",\n \"on emacs's cycle-spacing On first call, remove all whitespace (if any) from around\",\n \"= buffer.text # isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace())\",\n \"copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def\",\n \"line in enumerate(document.lines): if start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] +\",\n \"= event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0)\",\n \"accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event):\",\n \"On first call, remove all whitespace (if any) from around the cursor and\",\n \"file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command\",\n \"buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the\",\n \"call, remove all whitespace. On third call, restore the original whitespace and cursor\",\n \"= event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -=\",\n \"if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return\",\n \"from prompt_toolkit.key_binding.bindings.named_commands import (accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic\",\n \"\\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the end\",\n \"cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def\",\n \"row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if\",\n \"group that will match # the empty string. # Otherwise it will not\",\n \"# quoted strings (which will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) #\",\n \"row += 1 matching, mismatching = matching_parens(text) for opening, closing in matching: if\",\n \"buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 +\",\n \"new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) # Even though we do auto_up,\",\n \"emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\\",\n \"before cursor on the line must be whitespace because of the # TabShouldInsertWhitespaceFilter.\",\n \"row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening,\",\n \"# Some terminals (Like iTerm2) seem to paste \\\\r\\\\n line endings in a\",\n \"None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection()\",\n \"Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab\",\n \"= [] for (row, col, msg, m) in warnings: # Handle SyntaxErrorMessage which\",\n \"forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row, col =\",\n \"= new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS) @r.add_binding(Keys.ControlX,\",\n \"= None for m in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0)\",\n \"can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError:\",\n \"Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<')\",\n \"open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to S-Enter in iTerm2\",\n \"left them. If either of those # have changed, reset. The state here\",\n \"Out[2]: 1 ... ... In [3]: def test(): ... ...: pass ... ...:\",\n \"= document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left\",\n \"\\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >=\",\n \"from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess\",\n \"used to trigger autocompletion while typing. \\\"\\\"\\\" # Original text & cursor position.\",\n \"buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def\",\n \"document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors\",\n \"getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if\",\n \"blank_lines_after = 0 for line in lines_after_current: if not line.strip(): blank_lines_after += 1\",\n \"@r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[\",\n \"no prompt if match.group('ps1prompt') is not None: return '\\\\r' + match.group('line') + '\\\\n'\",\n \"[] for i, line in enumerate(document.lines): if start_line <= i < end_line: if\",\n \"= ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python 3.6\",\n \"data = data.replace('\\\\t', ' ') # Strip prompts off pasted text document =\",\n \"paste the text exactly) data = data.replace('\\\\t', ' ') # Strip prompts off\",\n \"len(rstripped) -1 text = rstripped + ' ' + lstripped state.append((text, cursor_position)) elif\",\n \"stripped. If no prompts are found the text is left alone. The resulting\",\n \"frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg, m) in warnings: # Handle\",\n \"re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"# have changed, reset. The state here is global, but that's fine, because\",\n \"get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline\",\n \"Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer\",\n \"indent the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))]\",\n \"it with a single space. On second call, remove all whitespace. On third\",\n \"not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def\",\n \"closing in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position\",\n \"this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could\",\n \"True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text)\",\n \"position as we last left them. If either of those # have changed,\",\n \"prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from\",\n \"in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines): if\",\n \"+ 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result())))\",\n \"prompt if match.group('ps1prompt') is not None: return '\\\\r' + match.group('line') + '\\\\n' elif\",\n \"find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8')\",\n \"pos = m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape,\",\n \"a newline at the end of the file, but I don't # think\",\n \"False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around in\",\n \"min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document\",\n \"# marker group that will match # the empty string. # Otherwise it\",\n \"else: copy_command = ['pbcopy'] try: # In Python 3.6 we can do this:\",\n \"give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if\",\n \"whitespace at the cursor (if any) with a single space. state.append((text, cursor_position)) cursor_position\",\n \"# This part is the same as the default binding # Some terminals\",\n \"not positions or positions[0] >= buffer.cursor_position: return p = positions[0] for pos in\",\n \"HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import\",\n \"text exactly) data = data.replace('\\\\t', ' ') # Strip prompts off pasted text\",\n \"-= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text =\",\n \"actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing\",\n \"document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down() row +=\",\n \"+ match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None: return match.group('line') + '\\\\n'\",\n \"or _x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous\",\n \"count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state:\",\n \"or shrinks below, keeping the cursor # position out of sync cursor_position =\",\n \"look exactly # as they did where we left off. # TODO: Use\",\n \"validate_text text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r')\",\n \"'\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure multilines end\",\n \"whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer\",\n \"+= 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before =\",\n \"len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. # Gives the\",\n \"indent, # Don't indent the first line, it's already indented lambda line, _x=[]:\",\n \"document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text, row, col): # We are\",\n \"pos = cursor_position - m.start(0) break else: pos = buffer.cursor_position if pos: deleted\",\n \":param fire_event: Fire `on_text_insert` event. This is mainly used to trigger autocompletion while\",\n \"subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event):\",\n \"len(text_before_cursor) - len(rstripped) -1 text = rstripped + ' ' + lstripped state.append((text,\",\n \"accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document = buffer.document cursor_line, cursor_col\",\n \"the same as the default binding # Some terminals (Like iTerm2) seem to\",\n \"Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset\",\n \"rstripped + ' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2: #\",\n \"start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1 else:\",\n \"def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param fire_event: Fire\",\n \"lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the\",\n \"= event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif\",\n \"p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to +\",\n \"the end will be stripped by the prompt anyway. # This just makes\",\n \"(?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the\",\n \"multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left()\",\n \"alone. The resulting text is indented by indent, except for the first line.\",\n \"occurs for unclosed single # quoted strings (which will give a syntax error)\",\n \"around the cursor and replace it with a single space. On second call,\",\n \"filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform():\",\n \"= 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text =\",\n \"to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste)\",\n \"= new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def\",\n \"all \\\\r's # have been stripped from # the string). (?P<line>.*)\\\\n # The\",\n \"def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape,\",\n \"whole # line. if m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos)\",\n \"pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions =\",\n \"== (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left)\",\n \"event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position =\",\n \"current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -=\",\n \"can be out of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text))\",\n \"because down is already at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable)\",\n \"Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event):\",\n \"HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x)\",\n \"'''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing prompts Replaces\",\n \"is that we resume when we shouldn't if things look exactly # as\",\n \"text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position = 0 if\",\n \"event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move\",\n \"cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def\",\n \"We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always\",\n \"m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) -\",\n \"start_line + 1 # Get the indentation for the comment delimiters min_indent =\",\n \"= document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer\",\n \"word.capitalize() because the first character could be # - or _ for i,\",\n \"event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position\",\n \"if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight)\",\n \"will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line\",\n \"original text and cursor position text, cursor_position = state[0] state.clear() if cursor_position <\",\n \"stripped by the prompt anyway. # This just makes this function easier to\",\n \"= True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event):\",\n \"prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version\",\n \"import CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure to use\",\n \"for the whole # line. if m.col != col: continue pos = document.translate_row_col_to_index(row,\",\n \"breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n'\",\n \"line. Also occurs for unclosed single # quoted strings (which will give a\",\n \"document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before +=\",\n \"prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from\",\n \"(because all \\\\r's # have been stripped from # the string). (?P<line>.*)\\\\n #\",\n \"Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position): state.clear() if len(state)\",\n \"1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def\",\n \"event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document\",\n \"def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow =\",\n \"emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess import sys import textwrap\",\n \"docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a single valid\",\n \"On nonblank line, delete any immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer\",\n \"text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state is the original\",\n \"= data.replace('\\\\t', ' ') # Strip prompts off pasted text document = buffer.document\",\n \"p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event):\",\n \"When tab should insert whitespace, do that instead of completion. \\\"\\\"\\\" # Text\",\n \"if not positions or positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos\",\n \"right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg\",\n \"stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection()\",\n \"buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer\",\n \"p = positions[-1] for pos in reversed(positions): if pos <= buffer.cursor_position: break p\",\n \"of inputs Outputs are stripped. If no prompts are found the text is\",\n \"is set to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] =\",\n \"col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event):\",\n \"return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text\",\n \"lines[1:]: # TODO: Send last chunk as bracketed paste, so it can be\",\n \"ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal\",\n \"Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ])\",\n \"buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS) @r.add_binding(Keys.ControlX, Keys.ControlC) def\",\n \"happens here is that we resume when we shouldn't if things look exactly\",\n \"range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end will\",\n \"buffer.text cursor_position = buffer.cursor_position pos = None for m in WORD.finditer(text): if m.end(0)\",\n \"around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg\",\n \"of verbose (x) option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\\",\n \"to escape all spaces here because of verbose (x) option below ps1_prompts =\",\n \"= document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1 else: start_line\",\n \"load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings\",\n \"row += 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent =\",\n \"event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt,\",\n \"prompts off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row +=\",\n \"matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos\",\n \"+= down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle\",\n \"(?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not # matched, this is a special\",\n \".theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess import\",\n \"event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text = buffer.text\",\n \"indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def\",\n \"text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore\",\n \"> cursor_position: pos = m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos)\",\n \"opening, closing in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1])\",\n \"textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure\",\n \"no carriage returns (\\\\r). Trailing whitespace and newlines is stripped from the outputs.\",\n \"else: paste_command = ['pbpaste'] try: # In Python 3.6 we can do this:\",\n \"cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer\",\n \"subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError\",\n \"try: validate_text(line) except SyntaxError: # If there is a syntax error, we can't\",\n \"settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/']\",\n \"> cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c')\",\n \"event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD =\",\n \"enumerate(lines): try: validate_text(line) except SyntaxError: # If there is a syntax error, we\",\n \"Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? #\",\n \"platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(),\",\n \"= buffer.text cursor_position = buffer.cursor_position pos = None for m in WORD.finditer(text): if\",\n \"= True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position =\",\n \"it matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after ==\",\n \"def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python, or IPython session\",\n \"else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data,\",\n \"if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state,\",\n \"'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\"\",\n \"cursor_line end_line = start_line + 1 # Get the indentation for the comment\",\n \"docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text, row,\",\n \"= document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text # isspace doesn't respect vacuous\",\n \"# run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not\",\n \"backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position\",\n \"is the # buffer text and cursor position as we last left them.\",\n \"== 1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if row\",\n \"filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def\",\n \"col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening, closing\",\n \"break p = pos event.current_buffer.cursor_position = p # This can be removed once\",\n \"... Out[2]: 1 ... ... In [3]: def test(): ... ...: pass ...\",\n \"<= buffer.cursor_position: return p = positions[-1] for pos in reversed(positions): if pos <=\",\n \"to intelligently add a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document =\",\n \"m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def\",\n \"= buffer.cursor_position pos = None for m in WORD.finditer(text): if m.end(0) > cursor_position:\",\n \"newlines at the end will be stripped by the prompt anyway. # This\",\n \"Replace all whitespace at the cursor (if any) with a single space. state.append((text,\",\n \"break blank_lines_after = 0 for line in lines_after_current: if not line.strip(): blank_lines_after +=\",\n \"?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and\",\n \"lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the cursor position correctly n_changed =\",\n \"# Don't overwrite the newline itself. Just before the line ending, # it\",\n \"else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX,\",\n \"the buffer, accept unless we are in a # docstring row, col =\",\n \"import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import\",\n \"we consider any change to be enough clear the state. The worst that\",\n \"not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else\",\n \"else: min_indent = 0 if min_indent == 0: break if min_indent == float('inf'):\",\n \"*\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE\",\n \"not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else:\",\n \"len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\"\",\n \"be stripped by the prompt anyway. # This just makes this function easier\",\n \"run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\\",\n \"Python, or IPython session and returns a list of inputs Outputs are stripped.\",\n \"See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs\",\n \"a b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\"\",\n \"return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back\",\n \"end of the file, but I don't # think it matters here. if\",\n \"text is indented by indent, except for the first line. It is assumed\",\n \"+ otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event):\",\n \"auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif\",\n \"prompts are found the text is left alone. The resulting text is indented\",\n \"== 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after == 0:\",\n \"'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete,\",\n \"- 1) end_line += 1 else: start_line = cursor_line end_line = start_line +\",\n \"+= up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer =\",\n \"\\\"\\\"\\\" Based on emacs's cycle-spacing On first call, remove all whitespace (if any)\",\n \"whitespace and newlines is stripped from the outputs. Example: >>> split_prompts(''' ... In\",\n \"C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash\",\n \"text.split('\\\\r') # Make sure multilines end in two newlines for i, line in\",\n \"'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if\",\n \"if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text\",\n \"indentation for the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if\",\n \"deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text =\",\n \"buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer =\",\n \"m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer text\",\n \"buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position -\",\n \"if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text =\",\n \"event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event):\",\n \"removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines with no prompt if\",\n \"uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position +=\",\n \"for clearing prompts Replaces PS1 prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\"\",\n \"always keeps a newline at the end of the file, but I don't\",\n \"p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a #\",\n \"pks[0].key == \\\"up\\\") or (len(pks) == 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key,\",\n \"autocompletion while typing. \\\"\\\"\\\" # Original text & cursor position. otext = buffer.text\",\n \"Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from\",\n \"Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event):\",\n \"text = buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0)\",\n \"left alone. The resulting text is indented by indent, except for the first\",\n \"we can't use the CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)] break\",\n \"at cursor position. :param fire_event: Fire `on_text_insert` event. This is mainly used to\",\n \"\\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around\",\n \"beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings,\",\n \"prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current\",\n \"if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if\",\n \"c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position\",\n \"buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer =\",\n \"0 for line in lines_after_current: if not line.strip(): blank_lines_after += 1 else: break\",\n \"for re.sub for clearing prompts Replaces PS1 prompts with \\\\r and removes PS2\",\n \"in enumerate(lines): try: validate_text(line) except SyntaxError: # If there is a syntax error,\",\n \"and inside_string(text, row, col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event)\",\n \"select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True\",\n \"Left that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0:\",\n \"> 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\",\",\n \"is already at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event):\",\n \"returns a list of inputs Outputs are stripped. If no prompts are found\",\n \"?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline\",\n \"indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i]\",\n \"position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when text grows or shrinks\",\n \"= m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer = event.current_buffer\",\n \"\\\\n as line ending. # This part is the same as the default\",\n \"new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document\",\n \"else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False)\",\n \"def transpose_lines(event): buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines =\",\n \"cursor (if any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) -\",\n \"1 else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before]\",\n \"PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing prompts Replaces PS1\",\n \"lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text\",\n \"PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, #\",\n \"line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the cursor position correctly\",\n \"from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import\",\n \"Send last chunk as bracketed paste, so it can be edited CMD_QUEUE.append(text) if\",\n \"event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send last chunk as bracketed paste,\",\n \"return p = positions[0] for pos in positions: if pos >= buffer.cursor_position: break\",\n \"text = buffer.text cursor_position = buffer.cursor_position pos = None for m in WORD.finditer(text):\",\n \"and returns a list of inputs Outputs are stripped. If no prompts are\",\n \"buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos\",\n \"buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position:\",\n \"= lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at\",\n \"platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In\",\n \"count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next =\",\n \"state.clear() if len(state) == 0: # Replace all whitespace at the cursor (if\",\n \"@r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text\",\n \"the original whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues\",\n \"\\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position\",\n \"chunk as bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event)\",\n \"buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This can be removed\",\n \"', '').endswith('\\\\n'): # If we are at the end of the buffer, accept\",\n \"worst that # happens here is that we resume when we shouldn't if\",\n \"0 if min_indent == 0: break if min_indent == float('inf'): min_indent = 0\",\n \"must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4\",\n \"where we left off. # TODO: Use event.previous_key_sequence instead. if state and state[-1]\",\n \"Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'),\",\n \"paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set\",\n \"blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before +\",\n \"positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer\",\n \"'c'] else: copy_command = ['pbcopy'] try: # In Python 3.6 we can do\",\n \"buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position\",\n \"@r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event):\",\n \"as the default binding # Some terminals (Like iTerm2) seem to paste \\\\r\\\\n\",\n \"save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete\",\n \"= p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning\",\n \"mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos\",\n \"up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if\",\n \"'\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python,\",\n \"like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data\",\n \"+ [''] + stripped_after) # Even though we do auto_up, it can be\",\n \"= event.current_buffer # Be sure to use \\\\n as line ending. # This\",\n \"# Text before cursor on the line must be whitespace because of the\",\n \"have been stripped from # the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE,\",\n \"for i, line in enumerate(document.lines): if start_line <= i < end_line: if uncomment:\",\n \"event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0)\",\n \"# TODO: Remove the lines with no prompt if match.group('ps1prompt') is not None:\",\n \"float('inf'): min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] == '#' for\",\n \"= True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move\",\n \"[emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING\",\n \"'accept_next', False) and ((len(pks) == 1 and pks[0].key == \\\"up\\\") or (len(pks) ==\",\n \"[r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\\",\n \"in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data =\",\n \"return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None: return match.group('line')\",\n \"WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper())\",\n \"from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from\",\n \"newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document)\",\n \"indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor)\",\n \"XXX: Emacs always keeps a newline at the end of the file, but\",\n \"buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection())\",\n \"1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) #\",\n \"# the empty string. # Otherwise it will not # match (because all\",\n \"min_indent = 0 if min_indent == 0: break if min_indent == float('inf'): min_indent\",\n \"end will be stripped by the prompt anyway. # This just makes this\",\n \"buffer = event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_,\",\n \"the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'(\",\n \"((len(pks) == 1 and pks[0].key == \\\"up\\\") or (len(pks) == 2 and pks[0].key\",\n \"text & cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite\",\n \"We don't need a parallel for down because down is already at the\",\n \"out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text,\",\n \"not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None\",\n \"# we consider any change to be enough clear the state. The worst\",\n \"cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if pos >\",\n \"m.end(0) > cursor_position: pos = m.end(0) - cursor_position break if pos: deleted =\",\n \"else: backward_delete_char(event) # Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, '\",\n \"# This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] !=\",\n \"here because of verbose (x) option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i)\",\n \"'\\\\n'.join(lines) # TODO: Set the cursor position correctly n_changed = 2*(cursor_line - start_line\",\n \"def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection()\",\n \"= buffer.cursor_position # Don't overwrite the newline itself. Just before the line ending,\",\n \"buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg if\",\n \"@r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position\",\n \"can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer =\",\n \"single # quoted strings (which will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer)\",\n \"from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import\",\n \"= event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text)\",\n \"clear the state. The worst that # happens here is that we resume\",\n \"cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank\",\n \"- m.start(0) break else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted)\",\n \"= re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position =\",\n \"# This just makes this function easier to test. lines = [i.rstrip() for\",\n \"paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is\",\n \"len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces\",\n \"cursor_position: pos = cursor_position - m.start(0) break else: pos = buffer.cursor_position if pos:\",\n \"global, but that's fine, because # we consider any change to be enough\",\n \"the text contains no carriage returns (\\\\r). Trailing whitespace and newlines is stripped\",\n \"if row == 0: buffer.cursor_down() row += 1 if row == len(new_lines) -\",\n \"<= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD)\",\n \"if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd')\",\n \"buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing\",\n \"event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg\",\n \"new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after == 0: return else:\",\n \"@r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow\",\n \"msg, m) in warnings: # Handle SyntaxErrorMessage which is the same warning for\",\n \"= buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\"\",\n \"input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as\",\n \"end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:])\",\n \"Don't use word.capitalize() because the first character could be # - or _\",\n \"+= n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS) @r.add_binding(Keys.ControlX, Keys.ControlC) def noop(event):\",\n \"= document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event)\",\n \"= document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row ==\",\n \"if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False):\",\n \"@r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow\",\n \"document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line +=\",\n \"is the same as the default binding # Some terminals (Like iTerm2) seem\",\n \"accept unless we are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row\",\n \"# lines correctly. # Gives the positions right before one or more blank\",\n \"= True if not positions or positions[-1] <= buffer.cursor_position: return p = positions[-1]\",\n \"'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer)\",\n \"def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command\",\n \"TODO: Set the cursor position correctly n_changed = 2*(cursor_line - start_line + 1)\",\n \"to use \\\\n as line ending. # This part is the same as\",\n \"') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first call, remove all\",\n \"= buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text)\",\n \"# This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released\",\n \"event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state\",\n \"keeping the cursor # position out of sync cursor_position = buffer.cursor_position buffer.cursor_position =\",\n \"inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else ''\",\n \"- 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event)\",\n \"because of verbose (x) option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) +\",\n \"if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up)\",\n \"Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. # Gives the positions right\",\n \"parallel for down because down is already at the end of the #\",\n \"enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word)\",\n \"text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at the\",\n \"The worst that # happens here is that we resume when we shouldn't\",\n \"if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters\",\n \"data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip',\",\n \"the default binding # Some terminals (Like iTerm2) seem to paste \\\\r\\\\n line\",\n \"of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not # matched, this\",\n \"CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip():\",\n \"insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set\",\n \"buffer = event.current_buffer # Be sure to use \\\\n as line ending. #\",\n \"for m in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word =\",\n \"line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines):\",\n \"load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters\",\n \"(x) option below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for\",\n \"def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if\",\n \"'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO)\",\n \"cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could\",\n \"# match (because all \\\\r's # have been stripped from # the string).\",\n \"elif len(state) == 2: # Exactly one space at the cursor. Remove it.\",\n \"continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions\",\n \"and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at the end of the\",\n \"Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] =\",\n \"Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding blank lines, leaving\",\n \"= event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position:\",\n \"as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command\",\n \"delete all surrounding blank lines, leaving just one. On isolated blank line, delete\",\n \"elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before\",\n \"buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped\",\n \"['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text\",\n \"i, j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\\",\n \"lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space at the\",\n \"search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on\",\n \"do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error:\",\n \"whitespace (if any) from around the cursor and replace it with a single\",\n \"Emacs always keeps a newline at the end of the file, but I\",\n \"default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state\",\n \"line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines)\",\n \"of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def\",\n \"if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else:\",\n \"enough clear the state. The worst that # happens here is that we\",\n \"+ line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the cursor position\",\n \"# Don't use word.capitalize() because the first character could be # - or\",\n \"= text[:cursor_position] # The first element of state is the original text. The\",\n \"clearing prompts Replaces PS1 prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\" #\",\n \"will be stripped by the prompt anyway. # This just makes this function\",\n \"buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS)\",\n \"tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the history search text\",\n \"it will not # match (because all \\\\r's # have been stripped from\",\n \"= 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else:\",\n \"if not positions or positions[0] >= buffer.cursor_position: return p = positions[0] for pos\",\n \"ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE\",\n \"load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event):\",\n \"Gives the positions right before one or more blank lines BLANK_LINES = re.compile(r'\\\\S\",\n \"so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event):\",\n \"document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text,\",\n \"from the outputs. Example: >>> split_prompts(''' ... In [1]: a = 1 ...\",\n \"@r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to escape all spaces\",\n \"getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching)\",\n \"them. If either of those # have changed, reset. The state here is\",\n \"left off. # TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text,\",\n \"could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr)\",\n \"buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text):\",\n \"text and cursor position text, cursor_position = state[0] state.clear() if cursor_position < 0:\",\n \"1 else: break blank_lines_after = 0 for line in lines_after_current: if not line.strip():\",\n \"= lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the end of\",\n \"split_prompts(''' ... In [1]: a = 1 ... ... In [2]: a ...\",\n \"lines, leaving just one. On isolated blank line, delete that one. On nonblank\",\n \"event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position\",\n \"if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text)\",\n \"is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B']\",\n \"from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as\",\n \"with \\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines with\",\n \"Remove it. cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position)) elif\",\n \"= len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line,\",\n \"event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None\",\n \"forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position\",\n \"PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) #\",\n \"indent) # Extraneous newlines at the end will be stripped by the prompt\",\n \"min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break if min_indent\",\n \"?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE =\",\n \"warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg, m) in\",\n \"otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just\",\n \"= buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer\",\n \"line in enumerate(lines): try: validate_text(line) except SyntaxError: # If there is a syntax\",\n \"Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[\",\n \"event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if\",\n \"event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\"\",\n \"cursor position. :param fire_event: Fire `on_text_insert` event. This is mainly used to trigger\",\n \"Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True)\",\n \"m in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) - cursor_position break\",\n \"= len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer =\",\n \"== 0: # Replace all whitespace at the cursor (if any) with a\",\n \"valid line. Also occurs for unclosed single # quoted strings (which will give\",\n \"0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection()\",\n \"in two newlines for i, line in enumerate(lines): try: validate_text(line) except SyntaxError: #\",\n \"- or _ for i, c in enumerate(word): if c.isalnum(): word = word[:i]\",\n \"match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None: return match.group('line') + '\\\\n' return\",\n \"be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit.\",\n \"event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position()\",\n \"cursor_position = 0 if cursor_position > len(text): cursor_position = len(text) return text, cursor_position\",\n \"?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE\",\n \"def comment(event): buffer = event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if\",\n \"except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0],\",\n \"bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer = event.current_buffer # Be\",\n \"run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/\",\n \"ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next\",\n \"if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return\",\n \"strings (which will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept\",\n \"indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python, or IPython session and returns\",\n \"# M-[ a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter =\",\n \"pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ)\",\n \"with a single space. On second call, remove all whitespace. On third call,\",\n \"@r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False)\",\n \"word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text\",\n \"import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from\",\n \"paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?) in\",\n \"- 1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text\",\n \"KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching\",\n \"data = data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces (C-x C-y will\",\n \"True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next\",\n \"buffer, accept unless we are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position)\",\n \"# line. if m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return\",\n \"def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def\",\n \"event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to escape all\",\n \"if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text\",\n \"new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\"\",\n \"False) def undo(event): event.current_buffer.undo() # Need to escape all spaces here because of\",\n \"m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position - m.start(0) break\",\n \"lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip():\",\n \"out of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text =\",\n \"True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer =\",\n \"which is the same warning for the whole # line. if m.col !=\",\n \"@r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position\",\n \"in positions: if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True\",\n \"@r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding blank lines,\",\n \"import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState\",\n \"for i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If there is\",\n \"one. On isolated blank line, delete that one. On nonblank line, delete any\",\n \"at the cursor. Remove it. cursor_position -= 1 text = rstripped + lstripped\",\n \"buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position\",\n \"forward one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"end in two newlines for i, line in enumerate(lines): try: validate_text(line) except SyntaxError:\",\n \"of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]):\",\n \"return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to\",\n \"copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if\",\n \"= 0 for line in lines_after_current: if not line.strip(): blank_lines_after += 1 else:\",\n \"This part is the same as the default binding # Some terminals (Like\",\n \"-1 text = rstripped + ' ' + lstripped state.append((text, cursor_position)) elif len(state)\",\n \"# run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could\",\n \"match.group('ps2prompt') is not None: return match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''):\",\n \"and blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after)\",\n \"\\\"\\\"\\\" Move back to the beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line\",\n \"def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection())\",\n \"(not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are at\",\n \"by the prompt anyway. # This just makes this function easier to test.\",\n \"@r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data\",\n \"that the text contains no carriage returns (\\\\r). Trailing whitespace and newlines is\",\n \"until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e:\",\n \"+ ' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly\",\n \"= re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the\",\n \"textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(),\",\n \"text_before_cursor = document.text_before_cursor text = buffer.text # isspace doesn't respect vacuous truth if\",\n \"Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0]\",\n \"prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing prompts Replaces PS1 prompts with\",\n \"buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer\",\n \"or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event):\",\n \"buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in\",\n \"and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines): if start_line <=\",\n \"opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps\",\n \"return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython, Python, or\",\n \"that we resume when we shouldn't if things look exactly # as they\",\n \"state and state[-1] != (text, cursor_position): state.clear() if len(state) == 0: # Replace\",\n \"= event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True\",\n \"['pbpaste'] try: # In Python 3.6 we can do this: # run(paste_command, input=text,\",\n \"If the prompt is not # matched, this is a special # marker\",\n \"data.replace('\\\\t', ' ') # Strip prompts off pasted text document = buffer.document row,\",\n \"from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to -\",\n \"def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>')\",\n \"blank line, delete that one. On nonblank line, delete any immediately following blank\",\n \"intelligently add a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document\",\n \"end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1 else: start_line = cursor_line\",\n \"1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if\",\n \"if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda\",\n \"resume when we shouldn't if things look exactly # as they did where\",\n \"= 0 uncomment = (all(not line.strip() or line[min_indent] == '#' for line in\",\n \"\\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0)\",\n \"pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer text\",\n \"undo(event): event.current_buffer.undo() # Need to escape all spaces here because of verbose (x)\",\n \"the prompt anyway. # This just makes this function easier to test. lines\",\n \"+ 1) if cursor_line >= end_line - 1: n_changed -= 2 if uncomment:\",\n \"= [] for i, line in enumerate(document.lines): if start_line <= i < end_line:\",\n \"line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in\",\n \"newlines for i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If there\",\n \"def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not\",\n \"+= 1 matching, mismatching = matching_parens(text) for opening, closing in matching: if opening.start\",\n \"def indent(event): \\\"\\\"\\\" When tab should insert whitespace, do that instead of completion.\",\n \"indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0:\",\n \"if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer =\",\n \"+ lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space at\",\n \"is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event):\",\n \"0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def\",\n \"\\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count =\",\n \"Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text =\",\n \"state is the original text. The last element is the # buffer text\",\n \"new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS) @r.add_binding(Keys.ControlX, Keys.ControlC)\",\n \"- cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0\",\n \"last left them. If either of those # have changed, reset. The state\",\n \"+ [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?',\",\n \"version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up)\",\n \"= do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip()\",\n \"we shouldn't if things look exactly # as they did where we left\",\n \"filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\"\",\n \"len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be\",\n \"ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\"\",\n \"SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection())\",\n \"n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app) @r.add_binding(Keys.ControlX, Keys.ControlS) @r.add_binding(Keys.ControlX, Keys.ControlC) def noop(event): pass\",\n \"Also occurs for unclosed single # quoted strings (which will give a syntax\",\n \"down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text) #\",\n \"from .processors import get_pyflakes_warnings import re import subprocess import sys import textwrap import\",\n \"buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def\",\n \"error, we can't use the CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)]\",\n \"r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts))\",\n \"'}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\" text =\",\n \"event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\"\",\n \"the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not # matched, this is\",\n \"cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped + ' ' +\",\n \"import get_pyflakes_warnings import re import subprocess import sys import textwrap import platform def\",\n \"FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\",\",\n \"= re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to the\",\n \"cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event):\",\n \"buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not\",\n \"\\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line)\",\n \"m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position =\",\n \"event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to S-Enter in iTerm2 settings\",\n \"= LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()):\",\n \"don't # think it matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before\",\n \"prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import\",\n \"if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event):\",\n \"buffer.cursor_down() row += 1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1]\",\n \"= event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state:\",\n \"lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line, it's already indented\",\n \"a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r',\",\n \"before one or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}')\",\n \"line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent\",\n \"= event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor =\",\n \"blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before +\",\n \"PS1 prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the\",\n \"cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos =\",\n \"= \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484\",\n \"# TODO: Send last chunk as bracketed paste, so it can be edited\",\n \"else: accept_line(event) elif not multiline: # Always accept a single valid line. Also\",\n \"mainly used to trigger autocompletion while typing. \\\"\\\"\\\" # Original text & cursor\",\n \"stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs\",\n \"match (because all \\\\r's # have been stripped from # the string). (?P<line>.*)\\\\n\",\n \"= event.data buffer = event.current_buffer # Be sure to use \\\\n as line\",\n \"characters at cursor position. :param fire_event: Fire `on_text_insert` event. This is mainly used\",\n \"\\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event):\",\n \"newline at the end of the file, but I don't # think it\",\n \"before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape,\",\n \"event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count)\",\n \"comment(event): buffer = event.current_buffer document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection:\",\n \"event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next\",\n \"import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import\",\n \"# as they did where we left off. # TODO: Use event.previous_key_sequence instead.\",\n \"four spaces (C-x C-y will still paste the text exactly) data = data.replace('\\\\t',\",\n \"len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down()\",\n \"Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser\",\n \"position out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position =\",\n \"# XXX: Emacs always keeps a newline at the end of the file,\",\n \"not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if\",\n \"warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <=\",\n \"positions right before one or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)')\",\n \"event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg, m)\",\n \"# Original text & cursor position. otext = buffer.text ocpos = buffer.cursor_position #\",\n \"Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command,\",\n \"text = buffer.text # isspace doesn't respect vacuous truth if (not text_after_cursor or\",\n \"once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] =\",\n \"it can be out of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position,\",\n \"Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state:\",\n \"word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,\",\n \"@r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in\",\n \"stripped from # the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def\",\n \"= event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count)\",\n \"2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text = new_text\",\n \"== \\\"up\\\") or (len(pks) == 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str)\",\n \"# Otherwise it will not # match (because all \\\\r's # have been\",\n \"a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline =\",\n \"= re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt>\",\n \"len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position =\",\n \"backward_kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m in\",\n \"empty string. # Otherwise it will not # match (because all \\\\r's #\",\n \"= textwrap.indent(lines[0], indent, # Don't indent the first line, it's already indented lambda\",\n \"def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for clearing prompts Replaces PS1 prompts\",\n \"though we do auto_up, it can be out of bounds from trailing #\",\n \"event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future = run_in_terminal(system_paste) event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda\",\n \"on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r\",\n \"redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to escape\",\n \"this function easier to test. lines = [i.rstrip() for i in lines] return\",\n \"i, line in enumerate(document.lines): if start_line <= i < end_line: if uncomment: lines.append(line[:min_indent]\",\n \"when text grows or shrinks below, keeping the cursor # position out of\",\n \"= event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position:\",\n \"text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) >\",\n \"'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for\",\n \"in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter\",\n \"1 and pks[0].key == \\\"up\\\") or (len(pks) == 2 and pks[0].key == \\\"escape\\\"\",\n \"SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) #\",\n \"'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if\",\n \"buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document\",\n \"= [i.rstrip() for i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython\",\n \"return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all\",\n \"import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(),\",\n \"in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if\",\n \"or IPython session and returns a list of inputs Outputs are stripped. If\",\n \"event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position\",\n \"merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from\",\n \"paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX,\",\n \"m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one\",\n \"first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i\",\n \"the line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We\",\n \"Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P')\",\n \"try: # In Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8')\",\n \"already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)):\",\n \"= 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event):\",\n \"multiline, execute. When in multiline, try to intelligently add a newline or execute.\",\n \"= new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event):\",\n \"buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if\",\n \"Set the cursor position correctly n_changed = 2*(cursor_line - start_line + 1) if\",\n \"buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\",\",\n \"'<') def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape,\",\n \"m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because\",\n \"tab should insert whitespace, do that instead of completion. \\\"\\\"\\\" # Text before\",\n \"else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) # Even though\",\n \"start_line + 1) if cursor_line >= end_line - 1: n_changed -= 2 if\",\n \"if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document = buffer.document\",\n \"while typing. \\\"\\\"\\\" # Original text & cursor position. otext = buffer.text ocpos\",\n \"buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position:\",\n \"if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer\",\n \"text is left alone. The resulting text is indented by indent, except for\",\n \"# Make sure multilines end in two newlines for i, line in enumerate(lines):\",\n \"buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer\",\n \"prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e:\",\n \"Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines\",\n \"buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could be # -\",\n \"... In [1]: a = 1 ... ... In [2]: a ... Out[2]:\",\n \"before the line ending, # it should act like insert mode. overwritten_text =\",\n \"subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError\",\n \"in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?',\",\n \"multiline and inside_string(text, row, col): # We are inside a docstring auto_newline(event.current_buffer) else:\",\n \"= Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When\",\n \"e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to =\",\n \"= len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph\",\n \"capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in\",\n \"load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document\",\n \"return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text =\",\n \"lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps\",\n \"... ...: pass ... ...: ... ''') ['a = 1', 'a', 'def test():\\\\n\",\n \"import inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import\",\n \"return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event):\",\n \"# We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: #\",\n \"+= 1 else: break blank_lines_after = 0 for line in lines_after_current: if not\",\n \"= event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def\",\n \"if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent,\",\n \"document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col,\",\n \"element is the # buffer text and cursor position as we last left\",\n \"input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0],\",\n \"in enumerate(document.lines): if start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:])\",\n \"a special # marker group that will match # the empty string. #\",\n \"new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if row == len(new_lines)\",\n \"This is mainly used to trigger autocompletion while typing. \\\"\\\"\\\" # Original text\",\n \"Make sure multilines end in two newlines for i, line in enumerate(lines): try:\",\n \"style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not\",\n \"0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1 else: break\",\n \"for line in lines_after_current: if not line.strip(): blank_lines_after += 1 else: break if\",\n \"from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip()\",\n \"True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if\",\n \"from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT)\",\n \"event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) ==\",\n \"break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer =\",\n \"to the beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor\",\n \"if start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent]\",\n \"= ['pbpaste'] try: # In Python 3.6 we can do this: # run(paste_command,\",\n \"history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position =\",\n \"'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m\",\n \"from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX, Keys.ControlY) def paste_from_clipboard(event): paste_text_future =\",\n \"buffer.cursor_position pos = None for m in WORD.finditer(text): if m.end(0) > cursor_position: pos\",\n \"len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph of\",\n \"S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] =\",\n \"toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None\",\n \"we are at the end of the buffer, accept unless we are in\",\n \"ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just before the line\",\n \"+ '\\\\n' elif match.group('ps2prompt') is not None: return match.group('line') + '\\\\n' return ''\",\n \"...: ... ''') ['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython\",\n \"else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send last\",\n \"positions[-1] for pos in reversed(positions): if pos <= buffer.cursor_position: break p = pos\",\n \"= ['pbcopy'] try: # In Python 3.6 we can do this: # run(copy_command,\",\n \"'').endswith('\\\\n'): # If we are at the end of the buffer, accept unless\",\n \"2*(cursor_line - start_line + 1) if cursor_line >= end_line - 1: n_changed -=\",\n \"accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if the previous key was\",\n \"in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline\",\n \"anyway. # This just makes this function easier to test. lines = [i.rstrip()\",\n \"cursor_position): state.clear() if len(state) == 0: # Replace all whitespace at the cursor\",\n \"min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] ==\",\n \"state[0] state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position > len(text):\",\n \"WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) - cursor_position break if pos:\",\n \"document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text\",\n \"row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text, row, col):\",\n \"pos = None for m in WORD.finditer(text): if m.end(0) > cursor_position: pos =\",\n \"validate_text(line) except SyntaxError: # If there is a syntax error, we can't use\",\n \"= buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position.\",\n \"for text in lines[1:]: # TODO: Send last chunk as bracketed paste, so\",\n \"platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In Python\",\n \"already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0])\",\n \"not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if\",\n \"1 ... ... In [3]: def test(): ... ...: pass ... ...: ...\",\n \"surrounding blank lines, leaving just one. On isolated blank line, delete that one.\",\n \"(which will give a syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the\",\n \"document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to\",\n \"is assumed that the text contains no carriage returns (\\\\r). Trailing whitespace and\",\n \"filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When in multiline, try\",\n \"pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions =\",\n \"Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple\",\n \"buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text = buffer.text\",\n \"= m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\"\",\n \"# the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\"\",\n \"ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This\",\n \"run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python)\",\n \"text and cursor position as we last left them. If either of those\",\n \"if document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col =\",\n \"end_line += 1 else: start_line = cursor_line end_line = start_line + 1 #\",\n \"text grows or shrinks below, keeping the cursor # position out of sync\",\n \"col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def\",\n \"we last left them. If either of those # have changed, reset. The\",\n \"i in lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data\",\n \"data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces (C-x C-y will still paste\",\n \"it. cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state)\",\n \"in lines[1:]: # TODO: Send last chunk as bracketed paste, so it can\",\n \"len(state) == 2: # Exactly one space at the cursor. Remove it. cursor_position\",\n \"= buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position:\",\n \"buffer.delete_before_cursor(count=4 + spaces%-4) else: backward_delete_char(event) # Reset the history search text buffer.history_search_text =\",\n \"if buffer.document.cursor_position_row > 0: buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if\",\n \"buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position = 0 @r.add_binding(Keys.Delete, filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection()\",\n \"work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda\",\n \"Just before the line ending, # it should act like insert mode. overwritten_text\",\n \"stripped_after) elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text =\",\n \"True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to\",\n \"buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document\",\n \"self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data)\",\n \"= document.translate_index_to_position(to - 1) end_line += 1 else: start_line = cursor_line end_line =\",\n \"lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer =\",\n \"vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If\",\n \"def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up\",\n \"None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row <\",\n \"buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if pos\",\n \"leaving just one. On isolated blank line, delete that one. On nonblank line,\",\n \"blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current\",\n \"new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def open_in_editor(event): event.current_buffer.open_in_editor(event.app)\",\n \"paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def\",\n \"if len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1\",\n \"that # happens here is that we resume when we shouldn't if things\",\n \"the cursor. Remove it. cursor_position -= 1 text = rstripped + lstripped state.append((text,\",\n \"beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def\",\n \"= event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the beginning\",\n \"on the line must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor =\",\n \"def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition(\",\n \"buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg if\",\n \"prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit\",\n \"str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter)\",\n \"opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos return event.app.output.bell()\",\n \"don't need a parallel for down because down is already at the end\",\n \"(?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not #\",\n \"lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send last chunk\",\n \"and newlines is stripped from the outputs. Example: >>> split_prompts(''' ... In [1]:\",\n \"+= len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position\",\n \"\\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines with no\",\n \"forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, '<') def beginning(event):\",\n \"exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda:\",\n \"issues when text grows or shrinks below, keeping the cursor # position out\",\n \"1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after == 0: return\",\n \"buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) # Even though we\",\n \"pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True):\",\n \"text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if\",\n \"document = buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range()\",\n \"removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A']\",\n \"getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key == \\\"up\\\") or (len(pks)\",\n \"' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first call, remove\",\n \"len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break if min_indent ==\",\n \"if min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip() or line[min_indent]\",\n \"as line ending. # This part is the same as the default binding\",\n \"def downcase_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m\",\n \"correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line >= end_line -\",\n \"word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text\",\n \"event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def kill_word(event): buffer =\",\n \"new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline.\",\n \"= text.split('\\\\r') # Make sure multilines end in two newlines for i, line\",\n \"current_line_indent, # Don't indent the first line, it's already indented lambda line, _x=[]:\",\n \"the cursor # position out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0\",\n \"load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import\",\n \"print(\\\"Error: could not find\\\", paste_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e,\",\n \"= new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around in\",\n \"lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward\",\n \"buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position +=\",\n \"if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position\",\n \"'\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent\",\n \"event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer\",\n \"> cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u')\",\n \"in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def\",\n \"if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4\",\n \"'\\\\n'.join(stripped_before + [''] + stripped_after) # Even though we do auto_up, it can\",\n \"+ lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and\",\n \"https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up)\",\n \"whitespace. On third call, restore the original whitespace and cursor position. \\\"\\\"\\\" buffer\",\n \"tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji, emoji_pudb from\",\n \"buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed\",\n \"= buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position\",\n \"not in multiline, execute. When in multiline, try to intelligently add a newline\",\n \"in platform.platform(): copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: #\",\n \"new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer\",\n \"'{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text =\",\n \"start_line <= i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] +\",\n \"last element is the # buffer text and cursor position as we last\",\n \"from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app from\",\n \"multiple blank # lines correctly. # Gives the positions right before one or\",\n \"cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position) def do_cycle_spacing(text,\",\n \"@r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable =\",\n \"i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If there is a\",\n \"this is a special # marker group that will match # the empty\",\n \"= new_pos return event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document =\",\n \"None for m in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) -\",\n \"event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if\",\n \"@r.add_binding(Keys.Escape, '<') def beginning(event): \\\"\\\"\\\" Move to the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0\",\n \"= '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event): buffer = event.current_buffer\",\n \"TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position): state.clear() if\",\n \"the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and\",\n \"the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else:\",\n \"CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document = buffer.document cursor_line,\",\n \"those # have changed, reset. The state here is global, but that's fine,\",\n \"Remove the lines with no prompt if match.group('ps1prompt') is not None: return '\\\\r'\",\n \"warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >=\",\n \"\\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly.\",\n \"text)).lstrip() lines = text.split('\\\\r') # Make sure multilines end in two newlines for\",\n \"buffer = event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before\",\n \"= event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row, col, msg,\",\n \"cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position =\",\n \"= event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1) @r.add_binding(Keys.Backspace,\",\n \"2: # Exactly one space at the cursor. Remove it. cursor_position -= 1\",\n \"p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning =\",\n \"except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in\",\n \"+ stripped_after) # Even though we do auto_up, it can be out of\",\n \"def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to\",\n \"'\\\\n') # Replace tabs with four spaces (C-x C-y will still paste the\",\n \"= min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break if\",\n \"if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need\",\n \"is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab']\",\n \"row, col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not\",\n \"PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at\",\n \"that will match # the empty string. # Otherwise it will not #\",\n \"(if any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped)\",\n \"# position out of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position\",\n \"by indent, except for the first line. It is assumed that the text\",\n \"from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from\",\n \"[] for (row, col, msg, m) in warnings: # Handle SyntaxErrorMessage which is\",\n \"doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'):\",\n \"+= 1 if multiline and inside_string(text, row, col): # We are inside a\",\n \"if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try:\",\n \"from # the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match):\",\n \"line, _x=[]: bool(_x or _x.append(1))) for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i],\",\n \"m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, 'd') def\",\n \"line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for\",\n \"default binding # Some terminals (Like iTerm2) seem to paste \\\\r\\\\n line endings\",\n \"character could be # - or _ for i, c in enumerate(word): if\",\n \"iTerm2) seem to paste \\\\r\\\\n line endings in a # bracketed paste. See:\",\n \"the original text. The last element is the # buffer text and cursor\",\n \"released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape,\",\n \"for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text)\",\n \"multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state,\",\n \"is mainly used to trigger autocompletion while typing. \\\"\\\"\\\" # Original text &\",\n \"outputs. Example: >>> split_prompts(''' ... In [1]: a = 1 ... ... In\",\n \"for i in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at\",\n \"any change to be enough clear the state. The worst that # happens\",\n \"= split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first\",\n \"it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer\",\n \"re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front\",\n \"Get the indentation for the comment delimiters min_indent = float('inf') for line in\",\n \"already at the end of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks\",\n \"(text, cursor_position): state.clear() if len(state) == 0: # Replace all whitespace at the\",\n \"of state is the original text. The last element is the # buffer\",\n \"be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 -\",\n \"at the end will be stripped by the prompt anyway. # This just\",\n \"= '\\\\n'.join(lines) # TODO: Set the cursor position correctly n_changed = 2*(cursor_line -\",\n \"positions or positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions:\",\n \"previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions\",\n \"= event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab\",\n \"deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor\",\n \"True if not positions or positions[0] >= buffer.cursor_position: return p = positions[0] for\",\n \"@r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of the line,\",\n \"and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape,\",\n \"for down because down is already at the end of the # prompt.\",\n \"two newlines for i, line in enumerate(lines): try: validate_text(line) except SyntaxError: # If\",\n \"file=sys.stderr) except subprocess.CalledProcessError as e: print(paste_command[0], \\\"error:\\\", e, file=sys.stderr) return p.stdout.decode('utf-8') @r.add_binding(Keys.ControlX, Keys.ControlW)\",\n \"think it matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after\",\n \"+= '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's\",\n \"= matching_parens(text) for opening, closing in matching: if closing.end == (row, col): new_pos\",\n \"]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning of the\",\n \"buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to the\",\n \"' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one\",\n \"buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer\",\n \"subprocess import sys import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings()\",\n \"state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first\",\n \"from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings\",\n \"TODO: Remove the lines with no prompt if match.group('ps1prompt') is not None: return\",\n \"@r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text)\",\n \"elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state =\",\n \"Handle SyntaxErrorMessage which is the same warning for the whole # line. if\",\n \"from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings\",\n \"cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding blank\",\n \"move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer text =\",\n \"= document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before\",\n \"m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position =\",\n \"if cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def\",\n \"+ '# ' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set\",\n \"= None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state =\",\n \"to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1)\",\n \"to be enough clear the state. The worst that # happens here is\",\n \"save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need to escape all spaces here\",\n \"completion. \\\"\\\"\\\" # Text before cursor on the line must be whitespace because\",\n \"in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer,\",\n \"def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row, col\",\n \"end of the buffer, accept unless we are in a # docstring row,\",\n \"cursor position as we last left them. If either of those # have\",\n \"\\\\r\\\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n',\",\n \"row += 1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] =\",\n \"Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because down is\",\n \"len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer\",\n \"@r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count\",\n \"= event.app.current_buffer document = buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before =\",\n \"stripped from the outputs. Example: >>> split_prompts(''' ... In [1]: a = 1\",\n \"+ '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure multilines\",\n \"the first character could be # - or _ for i, c in\",\n \"closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell()\",\n \"with four spaces (C-x C-y will still paste the text exactly) data =\",\n \"in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position =\",\n \"stripped_after) # Even though we do auto_up, it can be out of bounds\",\n \"sure multilines end in two newlines for i, line in enumerate(lines): try: validate_text(line)\",\n \"as bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape,\",\n \"event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline.\",\n \"is a special # marker group that will match # the empty string.\",\n \"len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position\",\n \"session and returns a list of inputs Outputs are stripped. If no prompts\",\n \"the line must be whitespace because of the # TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor\",\n \"return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text =\",\n \"# If we are at the end of the buffer, accept unless we\",\n \"encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True) except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr)\",\n \"cursor position correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line >=\",\n \"\\\"\\\"\\\" On blank line, delete all surrounding blank lines, leaving just one. On\",\n \"document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening, closing in matching:\",\n \"\\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is\",\n \"the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip():\",\n \"m.col != col: continue pos = document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p')\",\n \"# The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub\",\n \"else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line, it's already\",\n \"a syntax error, we can't use the CMD_QUEUE (it # breaks things). lines\",\n \"if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else:\",\n \"lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state is\",\n \"otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if\",\n \"Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g\",\n \"re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts\",\n \"min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent == 0: break\",\n \"\\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts = [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\\",\n \"delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent\",\n \"len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer = event.current_buffer\",\n \"new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection stuff @r.add_binding(Keys.ShiftLeft) def select_left(event):\",\n \"= True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position =\",\n \"filter=HasSelection()) @r.add_binding(Keys.Backspace, filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK,\",\n \"- m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f')\",\n \"= cursor_position - m.start(0) break else: pos = buffer.cursor_position if pos: deleted =\",\n \"multiline, try to intelligently add a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer\",\n \"terminals (Like iTerm2) seem to paste \\\\r\\\\n line endings in a # bracketed\",\n \"= event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position:\",\n \"= buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just before\",\n \"Example: >>> split_prompts(''' ... In [1]: a = 1 ... ... In [2]:\",\n \"'' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data,\",\n \"be # - or _ for i, c in enumerate(word): if c.isalnum(): word\",\n \"the whole # line. if m.col != col: continue pos = document.translate_row_col_to_index(row, col)\",\n \"auto_newline(event.current_buffer) # Always accept the line if the previous key was Up #\",\n \"import SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import\",\n \"isolated blank line, delete that one. On nonblank line, delete any immediately following\",\n \"= buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could be #\",\n \"we can do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True)\",\n \"for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position\",\n \"matching_parens(text) for opening, closing in matching: if opening.start == (row, col): new_pos =\",\n \"of sync cursor_position = buffer.cursor_position buffer.cursor_position = 0 buffer.text, buffer.cursor_position = do_cycle_spacing(buffer.text, cursor_position)\",\n \"cursor_position)) elif len(state) == 2: # Exactly one space at the cursor. Remove\",\n \"history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer\",\n \"a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape,\",\n \"delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def\",\n \"0: # Replace all whitespace at the cursor (if any) with a single\",\n \"in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the\",\n \"backward_delete_char(event) # Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ')\",\n \"(C-?) in iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash =\",\n \"# Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because down\",\n \"warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for (row,\",\n \"document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1:\",\n \"because the first character could be # - or _ for i, c\",\n \"lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's already indented\",\n \"event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\" if\",\n \"1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: #\",\n \"document.translate_index_to_position(to - 1) end_line += 1 else: start_line = cursor_line end_line = start_line\",\n \"cursor_position) def do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor =\",\n \"= document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching = matching_parens(text) for opening, closing in\",\n \"won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark,\",\n \"'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n'\",\n \"if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should\",\n \"Always accept the line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492.\",\n \"re.compile(r'( *)[^ ]?') @r.add_binding(Keys.Escape, 'm') def back_to_indentation(event): \\\"\\\"\\\" Move back to the beginning\",\n \"== len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines)\",\n \"Move back to the beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line =\",\n \"the # buffer text and cursor position as we last left them. If\",\n \"will not # match (because all \\\\r's # have been stripped from #\",\n \"# https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False)\",\n \"seem to paste \\\\r\\\\n line endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737\",\n \"matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position = new_pos\",\n \"event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position: return p\",\n \"getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps\",\n \"correctly @r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else:\",\n \"in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else:\",\n \"because # we consider any change to be enough clear the state. The\",\n \"in multiline, execute. When in multiline, try to intelligently add a newline or\",\n \"Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for down because\",\n \"rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text\",\n \"' for i, j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ '] ps2_prompts\",\n \"= text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of state is the\",\n \"event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one\",\n \"are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept\",\n \"new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down()\",\n \"off. # TODO: Use event.previous_key_sequence instead. if state and state[-1] != (text, cursor_position):\",\n \"state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and cursor position\",\n \"data = event.data buffer = event.current_buffer # Be sure to use \\\\n as\",\n \"front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is\",\n \"# merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo()\",\n \"import get_app from prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from\",\n \"= document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line\",\n \"c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() + word[i+1:].lower() break\",\n \"# whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event):\",\n \"event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and ((len(pks) == 1 and pks[0].key\",\n \"move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event. This\",\n \"= event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position =\",\n \"except FileNotFoundError: print(\\\"Error: could not find\\\", copy_command[0], file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0],\",\n \"insert whitespace, do that instead of completion. \\\"\\\"\\\" # Text before cursor on\",\n \"buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position:\",\n \"0 if cursor_position > len(text): cursor_position = len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO)\",\n \"event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event):\",\n \"Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @r.add_binding(Keys.ControlX,\",\n \"# Strip prompts off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position)\",\n \"multilines end in two newlines for i, line in enumerate(lines): try: validate_text(line) except\",\n \"all whitespace at the cursor (if any) with a single space. state.append((text, cursor_position))\",\n \"Strip prompts off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row\",\n \"0 uncomment = (all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line])\",\n \"in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] #\",\n \"prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If\",\n \"keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\"\",\n \"# docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and inside_string(text,\",\n \"lines with no prompt if match.group('ps1prompt') is not None: return '\\\\r' + match.group('line')\",\n \"kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos = None\",\n \"'\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE =\",\n \">>> split_prompts(''' ... In [1]: a = 1 ... ... In [2]: a\",\n \"it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in range(1,\",\n \"event.app.current_buffer # Avoid issues when text grows or shrinks below, keeping the cursor\",\n \"be out of bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text\",\n \"if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position\",\n \"positions: if pos >= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position\",\n \"True if not positions or positions[-1] <= buffer.cursor_position: return p = positions[-1] for\",\n \"not line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for line in\",\n \"to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank\",\n \"the positions right before one or more blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n\",\n \"indented by indent, except for the first line. It is assumed that the\",\n \"def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m\",\n \"the line ending, # it should act like insert mode. overwritten_text = otext[ocpos:ocpos\",\n \"+= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError,\",\n \"escape all spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\\\\",\n \"False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def keyboard_interrupt(event):\",\n \"break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def\",\n \"event. This is mainly used to trigger autocompletion while typing. \\\"\\\"\\\" # Original\",\n \"= re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph\",\n \"The resulting text is indented by indent, except for the first line. It\",\n \"prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection import SelectionState from prompt_toolkit.clipboard import ClipboardData\",\n \"inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a\",\n \"+ blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before + blank_lines_after\",\n \"pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if\",\n \"Text before cursor on the line must be whitespace because of the #\",\n \"The state here is global, but that's fine, because # we consider any\",\n \"document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent\",\n \"multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When in multiline, try to intelligently\",\n \"insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param fire_event: Fire `on_text_insert`\",\n \"= event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position\",\n \"filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks and getattr(pks[-1], 'accept_next', False) and\",\n \"blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + [''] +\",\n \"with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text\",\n \"= p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a\",\n \"+= 1 else: start_line = cursor_line end_line = start_line + 1 # Get\",\n \"state. The worst that # happens here is that we resume when we\",\n \"lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When\",\n \"The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl function for re.sub for\",\n \"and ((len(pks) == 1 and pks[0].key == \\\"up\\\") or (len(pks) == 2 and\",\n \"import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens from .theme import emoji,\",\n \"or positions[-1] <= buffer.cursor_position: return p = positions[-1] for pos in reversed(positions): if\",\n \"= event.current_buffer text = buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): pos\",\n \"def undo(event): event.current_buffer.undo() # Need to escape all spaces here because of verbose\",\n \"m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position =\",\n \"buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event):\",\n \"a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if multiline and\",\n \"prompts. \\\"\\\"\\\" # TODO: Remove the lines with no prompt if match.group('ps1prompt') is\",\n \"delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces = len(buffer.document.current_line_before_cursor) # Delete up to\",\n \"document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in enumerate(document.lines): if start_line\",\n \"event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) -\",\n \"not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor:\",\n \"in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if\",\n \"- indent.end(1) @r.add_binding(Keys.Backspace, save_before=if_no_repeat) def delete_char_or_unindent(event): buffer = event.app.current_buffer if buffer.document.current_line_before_cursor.isspace(): spaces =\",\n \"cursor. Remove it. cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position))\",\n \"copy_command = ['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python\",\n \"len(state) == 3: # Restore original text and cursor position text, cursor_position =\",\n \"`on_text_insert` event. This is mainly used to trigger autocompletion while typing. \\\"\\\"\\\" #\",\n \".mypython import CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure to\",\n \"prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from\",\n \"and pks[0].key == \\\"up\\\") or (len(pks) == 2 and pks[0].key == \\\"escape\\\" and\",\n \"a list of inputs Outputs are stripped. If no prompts are found the\",\n \"prompt anyway. # This just makes this function easier to test. lines =\",\n \"(C-x C-y will still paste the text exactly) data = data.replace('\\\\t', ' ')\",\n \"if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we are\",\n \"we are in a # docstring row, col = document.translate_index_to_position(buffer.cursor_position) row += 1\",\n \"file=sys.stderr) except subprocess.CalledProcessError as e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\"\",\n \"re.sub for clearing prompts Replaces PS1 prompts with \\\\r and removes PS2 prompts.\",\n \"inside_string, matching_parens from .theme import emoji, emoji_pudb from .processors import get_pyflakes_warnings import re\",\n \"break if min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip() or\",\n \"event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not\",\n \"position correctly n_changed = 2*(cursor_line - start_line + 1) if cursor_line >= end_line\",\n \"@r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if\",\n \"start_line = cursor_line end_line = start_line + 1 # Get the indentation for\",\n \"@r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text\",\n \"if cursor_position < 0: cursor_position = 0 if cursor_position > len(text): cursor_position =\",\n \"min_indent == 0: break if min_indent == float('inf'): min_indent = 0 uncomment =\",\n \"start_line, start_col = document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1\",\n \"contains no carriage returns (\\\\r). Trailing whitespace and newlines is stripped from the\",\n \"in multiline, try to intelligently add a newline or execute. \\\"\\\"\\\" buffer =\",\n \"= buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def\",\n \"This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is in a # released version\",\n \"['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event):\",\n \"# released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down)\",\n \"import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines =\",\n \"for i, c in enumerate(word): if c.isalnum(): word = word[:i] + c.capitalize() +\",\n \"+ stripped_after) elif blank_lines_before + blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text\",\n \"wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position +=\",\n \"elif not multiline: # Always accept a single valid line. Also occurs for\",\n \"is a syntax error, we can't use the CMD_QUEUE (it # breaks things).\",\n \"iTerm2 settings Keys.ControlQuestionmark = \\\"<C-?>\\\" ALL_KEYS.append(\\\"<C-?>\\\") ANSI_SEQUENCES['\\\\x1b[ab'] = Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\")\",\n \"m.start(0) break else: pos = buffer.cursor_position if pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def\",\n \"= True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event):\",\n \"@r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() #\",\n \"@r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first call,\",\n \"the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE)) def prompt_repl(match): r\\\"\\\"\\\" repl\",\n \"LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent\",\n \"insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer\",\n \"buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself. Just before the\",\n \"as prompt_toolkit_version from .multiline import (auto_newline, tab_should_insert_whitespace, document_is_multiline_python) from .tokenize import inside_string, matching_parens\",\n \"all whitespace. On third call, restore the original whitespace and cursor position. \\\"\\\"\\\"\",\n \"spaces%-4) else: backward_delete_char(event) # Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape,\",\n \".mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines\",\n \"indent the first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))\",\n \"auto_newline(event.current_buffer) else: accept_line(event) elif not multiline: # Always accept a single valid line.\",\n \"accept the line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. #\",\n \"event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape, 'n') def next_warning(event): positions = warning_positions(event)\",\n \"SyntaxErrorMessage which is the same warning for the whole # line. if m.col\",\n \"word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document\",\n \"inputs Outputs are stripped. If no prompts are found the text is left\",\n \"!= '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False)\",\n \"@r.add_binding(Keys.Escape, 'b') @r.add_binding(Keys.Escape, Keys.Left) def backward_word(event): \\\"\\\"\\\" Move back one paragraph of text\",\n \"position. :param fire_event: Fire `on_text_insert` event. This is mainly used to trigger autocompletion\",\n \"assumed that the text contains no carriage returns (\\\\r). Trailing whitespace and newlines\",\n \"# If there is a syntax error, we can't use the CMD_QUEUE (it\",\n \"same warning for the whole # line. if m.col != col: continue pos\",\n \"sure to use \\\\n as line ending. # This part is the same\",\n \"# Don't indent the first line, it's already indented lambda line, _x=[]: bool(_x\",\n \"pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return\",\n \"list of inputs Outputs are stripped. If no prompts are found the text\",\n \"correctly. # Gives the positions right before one or more blank lines BLANK_LINES\",\n \"word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event):\",\n \"lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the end of the\",\n \"\\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat\",\n \"tabs with four spaces (C-x C-y will still paste the text exactly) data\",\n \"= KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions =\",\n \"matched, this is a special # marker group that will match # the\",\n \"buffer.cursor_up(count=count) elif not buffer.selection_state: event.key_sequence[-1].accept_next = True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state\",\n \"= buffer.document cursor_line, cursor_col = document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line,\",\n \"if pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This\",\n \"buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer =\",\n \"0)) new_text = '\\\\n'.join(stripped_before + [''] + stripped_after) # Even though we do\",\n \"True buffer.history_backward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer\",\n \"-= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\"\",\n \"emacs's cycle-spacing On first call, remove all whitespace (if any) from around the\",\n \"buffer.cursor_position = 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not\",\n \"is not # matched, this is a special # marker group that will\",\n \"current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or PS2_PROMPTS_RE.match(data.strip()): lines =\",\n \"buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):] if move_cursor: buffer.cursor_position +=\",\n \"= start_line + 1 # Get the indentation for the comment delimiters min_indent\",\n \"isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ',\",\n \"blank lines BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move\",\n \"PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent\",\n \"will still paste the text exactly) data = data.replace('\\\\t', ' ') # Strip\",\n \"re import subprocess import sys import textwrap import platform def get_key_bindings(): # Based\",\n \"original whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when\",\n \"= pos event.current_buffer.cursor_position = p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857\",\n \"the first line. It is assumed that the text contains no carriage returns\",\n \"set to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter\",\n \"if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False):\",\n \"= len(text) - m.end(1) + 1 return event.current_buffer.cursor_position = 0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)')\",\n \"but I don't # think it matters here. if (not blank_lines_before and blank_lines_after)\",\n \"buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row < buffer.document.line_count - 1: buffer.cursor_down(count=count)\",\n \"# breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] +=\",\n \"+= 1 matching, mismatching = matching_parens(text) for opening, closing in matching: if closing.end\",\n \"text_before_cursor = text[:cursor_position] # The first element of state is the original text.\",\n \"closing in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1, closing.end[1]) buffer.cursor_position\",\n \"= len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position\",\n \"row == 0: buffer.cursor_down() row += 1 if row == len(new_lines) - 1:\",\n \"event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state\",\n \"of the file, but I don't # think it matters here. if (not\",\n \"of those # have changed, reset. The state here is global, but that's\",\n \"except for the first line. It is assumed that the text contains no\",\n \"= 0 @r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state:\",\n \"buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position =\",\n \"grows or shrinks below, keeping the cursor # position out of sync cursor_position\",\n \"text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') #\",\n \"of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))):\",\n \"word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer\",\n \"\\\"\\\"\\\" # Text before cursor on the line must be whitespace because of\",\n \"# Get the indentation for the comment delimiters min_indent = float('inf') for line\",\n \"n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text = new_text else: buffer.text\",\n \"In [2]: a ... Out[2]: 1 ... ... In [3]: def test(): ...\",\n \"!= '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert whitespace,\",\n \"for the first line. It is assumed that the text contains no carriage\",\n \"-= n_changed buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX,\",\n \"= event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command = ['xclip', '-selection',\",\n \"Insert characters at cursor position. :param fire_event: Fire `on_text_insert` event. This is mainly\",\n \"isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape,\",\n \"'3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def\",\n \"buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer =\",\n \"do_cycle_spacing(text, cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] #\",\n \"immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current =\",\n \"+ '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes text copied from mypython,\",\n \"cursor_position -= 1 text = rstripped + lstripped state.append((text, cursor_position)) elif len(state) ==\",\n \"line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines =\",\n \"cursor and replace it with a single space. On second call, remove all\",\n \"_x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]:\",\n \"a ... Out[2]: 1 ... ... In [3]: def test(): ... ...: pass\",\n \"cycle-spacing On first call, remove all whitespace (if any) from around the cursor\",\n \"pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.upper()) return\",\n \"is not None: return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is not\",\n \"or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = []\",\n \"positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning =\",\n \"\\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC, filter=~is_searching) def\",\n \"False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if\",\n \"[3]: def test(): ... ...: pass ... ...: ... ''') ['a = 1',\",\n \"def backward_word(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text\",\n \"+ [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji + [emoji_pudb]] +\",\n \"in WORD.finditer(text): if m.end(0) > cursor_position: pos = m.end(0) - cursor_position break if\",\n \"for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines = [] for i, line in\",\n \"cursor position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position =\",\n \"kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def system_copy(text): if \\\"Linux\\\" in platform.platform(): copy_command =\",\n \"CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer document =\",\n \"insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data +\",\n \"multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state,\",\n \"indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1) if indent else '' if PS1_PROMPTS_RE.match(data.strip()) or\",\n \"= len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. # Gives\",\n \"1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip()\",\n \"If there is a syntax error, we can't use the CMD_QUEUE (it #\",\n \"restore the original whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid\",\n \"float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if\",\n \"lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] =\",\n \"pass'] \\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text =\",\n \"word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event):\",\n \"event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None\",\n \"if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted) @r.add_binding(Keys.Escape, Keys.Backspace) def backward_kill_word(event): buffer = event.current_buffer\",\n \"= buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not down_position: buffer.cursor_position = len(buffer.document.text) # The\",\n \"event.current_buffer.cut_selection() paste_text_future.add_done_callback(lambda future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?)\",\n \"BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,\",\n \"= LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if\",\n \"Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work until\",\n \"event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text\",\n \"split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't indent the first line,\",\n \"from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import\",\n \"event.app.output.bell() @r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text =\",\n \"text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) <\",\n \"key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't need a parallel for\",\n \"if match.group('ps1prompt') is not None: return '\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt')\",\n \"+ line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line) new_text =\",\n \"indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for\",\n \"def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data buffer = event.current_buffer #\",\n \"= otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos + len(overwritten_text):]\",\n \"def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text\",\n \"# Handle SyntaxErrorMessage which is the same warning for the whole # line.\",\n \"textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure multilines end in two newlines\",\n \"= data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces (C-x C-y will still\",\n \"import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection,\",\n \"buffer.cursor_position # Don't overwrite the newline itself. Just before the line ending, #\",\n \"test(): ... ...: pass ... ...: ... ''') ['a = 1', 'a', 'def\",\n \"from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys,\",\n \"= 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to the end \\\"\\\"\\\" event.current_buffer.cursor_position\",\n \"in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position = 0\",\n \"lines correctly. # Gives the positions right before one or more blank lines\",\n \"= matching_parens(text) for opening, closing in matching: if opening.start == (row, col): new_pos\",\n \"@r.add_binding(Keys.ControlSpace) def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection()\",\n \"text in lines[1:]: # TODO: Send last chunk as bracketed paste, so it\",\n \"and cursor position as we last left them. If either of those #\",\n \"load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import\",\n \"if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.ControlD) def exit(event): event.app.exit(exception=EOFError, style='class:exiting') @r.add_binding(Keys.ControlC,\",\n \"buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp)\",\n \"(all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip()) lines\",\n \"1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if row ==\",\n \"= float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line)\",\n \"this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error:\",\n \"spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\\\ '] +\",\n \"at the end of the buffer, accept unless we are in a #\",\n \"if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't\",\n \"3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True) subprocess.run(copy_command, input=text.encode('utf-8'), check=True)\",\n \"exactly # as they did where we left off. # TODO: Use event.previous_key_sequence\",\n \"change to be enough clear the state. The worst that # happens here\",\n \"is the original text. The last element is the # buffer text and\",\n \"r\\\"\\\"\\\" Takes text copied from mypython, Python, or IPython session and returns a\",\n \">= end_line - 1: n_changed -= 2 if uncomment: buffer.cursor_position -= n_changed buffer.text\",\n \"cursor_position: pos = m.end(0) - cursor_position break if pos: deleted = buffer.delete(count=pos) event.app.clipboard.set_text(deleted)\",\n \"(it # breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n' in line.rstrip(): lines[i]\",\n \"def toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX,\",\n \"= m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize()\",\n \"\\\"\\\"\\\" # Original text & cursor position. otext = buffer.text ocpos = buffer.cursor_position\",\n \"bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';')\",\n \"... In [2]: a ... Out[2]: 1 ... ... In [3]: def test():\",\n \"be enough clear the state. The worst that # happens here is that\",\n \"Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row,\",\n \"+ len(overwritten_text):] if move_cursor: buffer.cursor_position += len(data) @r.add_binding(Keys.Escape, 'l') def downcase_word(event): buffer =\",\n \"def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow =\",\n \"@r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape,\",\n \"word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first character could be\",\n \"Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text row,\",\n \"event.current_buffer.cursor_left() # M-[ a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter\",\n \"= (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next =\",\n \"If either of those # have changed, reset. The state here is global,\",\n \"= True if not positions or positions[0] >= buffer.cursor_position: return p = positions[0]\",\n \"# of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt is not # matched,\",\n \"< buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False):\",\n \"buffer._show_syntax_warning = True if not positions or positions[0] >= buffer.cursor_position: return p =\",\n \"a parallel for down because down is already at the end of the\",\n \"# Reset the history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def\",\n \"= buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer\",\n \"min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] == '#' for line\",\n \"custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings\",\n \"buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position\",\n \"return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event): buffer = event.current_buffer document =\",\n \"None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first\",\n \"pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,\",\n \"line if the previous key was Up # Requires https://github.com/jonathanslenders/python-prompt-toolkit/pull/492. # We don't\",\n \"buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position += event.arg @r.add_binding(Keys.Up) def auto_up(event): buffer =\",\n \"first call, remove all whitespace (if any) from around the cursor and replace\",\n \"truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): # If we\",\n \"= state[0] state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position >\",\n \"M-[ a g is set to S-Enter in iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\"\",\n \"@r.add_binding(Keys.ControlO) def open_line(event): event.current_buffer.newline(copy_margin=False) event.current_buffer.cursor_left() # M-[ a g is set to S-Enter\",\n \"\\\"\\\"\\\" When not in multiline, execute. When in multiline, try to intelligently add\",\n \"blank_lines_after += 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before\",\n \"WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape,\",\n \"line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1))) for i in\",\n \"pos: deleted = buffer.delete_before_cursor(count=pos) event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at\",\n \"# Always accept a single valid line. Also occurs for unclosed single #\",\n \"# isspace doesn't respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace('\",\n \"and cursor position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position\",\n \"= event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0)\",\n \"end \\\"\\\"\\\" event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines\",\n \"if state and state[-1] != (text, cursor_position): state.clear() if len(state) == 0: #\",\n \"same as the default binding # Some terminals (Like iTerm2) seem to paste\",\n \"buffer.document.line_count - 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state\",\n \"(accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from\",\n \"state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space at the cursor.\",\n \"bounds from trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX,\",\n \"= text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The first element of\",\n \"buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer =\",\n \"act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] +\",\n \"makes this function easier to test. lines = [i.rstrip() for i in lines]\",\n \"= rstripped + lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original\",\n \"prompt_toolkit.key_binding.bindings.named_commands import (accept_line, self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import\",\n \"event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer text = buffer.text\",\n \"lines = text.split('\\\\r') # Make sure multilines end in two newlines for i,\",\n \"event.current_buffer document = buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row +=\",\n \"for opening, closing in matching: if opening.start == (row, col): new_pos = document.translate_row_col_to_index(closing.end[0]-1,\",\n \"def cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first call, remove all whitespace\",\n \"below ps1_prompts = [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j\",\n \"for the comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not\",\n \"= (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer\",\n \"cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position:\",\n \"the file, but I don't # think it matters here. if (not blank_lines_before\",\n \"toggle_selection(event): buffer = event.current_buffer if buffer.selection_state: buffer.selection_state = None else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h')\",\n \"returns (\\\\r). Trailing whitespace and newlines is stripped from the outputs. Example: >>>\",\n \"len(text) @r.add_binding(Keys.Escape, 'c') def capitalize_word(event): buffer = event.current_buffer text = buffer.text cursor_position =\",\n \"n_changed = 2*(cursor_line - start_line + 1) if cursor_line >= end_line - 1:\",\n \"len(new_lines) == 1: new_lines.append('') if row == 0: buffer.cursor_down() row += 1 if\",\n \"the text exactly) data = data.replace('\\\\t', ' ') # Strip prompts off pasted\",\n \"col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer =\",\n \"new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row =\",\n \"0 WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text\",\n \"def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions = [] for\",\n \"get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(),\",\n \"PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines with no prompt if match.group('ps1prompt')\",\n \"the beginning \\\"\\\"\\\" event.current_buffer.cursor_position = 0 @r.add_binding(Keys.Escape, '>') def end(event): \\\"\\\"\\\" Move to\",\n \"True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position += up_position if not up_position: buffer.cursor_position = 0\",\n \"resulting text is indented by indent, except for the first line. It is\",\n \"blank line, delete all surrounding blank lines, leaving just one. On isolated blank\",\n \"not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer\",\n \"overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos] + data + otext[ocpos +\",\n \"that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position\",\n \"lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be stripped\",\n \"in WORD.finditer(text): if m.end(0) > cursor_position: event.current_buffer.cursor_position = m.end(0) return event.current_buffer.cursor_position = len(text)\",\n \"\\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text[::-1]): if m.start(0)\",\n \"cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline\",\n \"nonblank line, delete any immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document\",\n \"and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines with no prompt\",\n \"load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def\",\n \".processors import get_pyflakes_warnings import re import subprocess import sys import textwrap import platform\",\n \"[re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji + [emoji_pudb]] + [r'In\\\\\",\n \"prompt_toolkit.key_binding.bindings.mouse import load_mouse_bindings from prompt_toolkit.key_binding.bindings.cpr import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding\",\n \"filter=HasSelection()) def delete_selection(event): event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU,\",\n \"= buffer.document row = document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('')\",\n \"import re import subprocess import sys import textwrap import platform def get_key_bindings(): #\",\n \"M-[ a b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark =\",\n \"buffer.document text = buffer.text row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 matching, mismatching\",\n \"not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] #\",\n \"from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings\",\n \"delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete all surrounding blank lines, leaving just one.\",\n \"# M-[ a b is set to C-S-/ (C-?) in iTerm2 settings Keys.ControlQuestionmark\",\n \"part is the same as the default binding # Some terminals (Like iTerm2)\",\n \"transpose_lines(event): buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:]\",\n \"around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position + event.arg <= len(event.current_buffer.text): event.current_buffer.cursor_position += event.arg\",\n \"... ...: ... ''') ['a = 1', 'a', 'def test():\\\\n pass'] \\\"\\\"\\\" from\",\n \"pos <= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This can\",\n \"uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' + line[min_indent:]) else: lines.append(line)\",\n \"I don't # think it matters here. if (not blank_lines_before and blank_lines_after) or\",\n \"cursor_position)) elif len(state) == 3: # Restore original text and cursor position text,\",\n \"'\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first line, it's already\",\n \"@r.add_binding(Keys.ControlX, Keys.ControlW) def copy_to_clipboard(event): if event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1]))\",\n \"line.strip(): blank_lines_before += 1 else: break blank_lines_after = 0 for line in lines_after_current:\",\n \"of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text):\",\n \"-= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state:\",\n \"# happens here is that we resume when we shouldn't if things look\",\n \"= textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make\",\n \"buffer = event.current_buffer document = buffer.document row = document.cursor_position_row new_lines = document.lines[:] if\",\n \"= event.current_buffer if buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position =\",\n \"= document.translate_index_to_position(document.cursor_position) if document.selection: from_, to = document.selection_range() start_line, start_col = document.translate_index_to_position(from_) end_line,\",\n \"= (all(not line.strip() or line[min_indent] == '#' for line in document.lines[start_line:end_line]) and ''.join(document.lines[start_line:end_line]).strip())\",\n \"pos event.current_buffer.cursor_position = p # This can be removed once # https://github.com/prompt-toolkit/python-prompt-toolkit/pull/857 is\",\n \"and state[-1] != (text, cursor_position): state.clear() if len(state) == 0: # Replace all\",\n \"trailing # whitespace buffer.cursor_position = min(buffer.cursor_position, len(new_text)) buffer.text = new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def\",\n \"elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def\",\n \"space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor) - len(rstripped) -1 text = rstripped +\",\n \"any immediately following blank lines. \\\"\\\"\\\" buffer = event.app.current_buffer document = buffer.document lines_up_to_current\",\n \"if pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the\",\n \"event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event): data = event.current_buffer.cut_selection() event.app.clipboard.set_data(data) def\",\n \"positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions: if pos\",\n \"buffer = event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next =\",\n \"(\\\\r). Trailing whitespace and newlines is stripped from the outputs. Example: >>> split_prompts('''\",\n \"paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace\",\n \"r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji + [emoji_pudb]] + [r'In\\\\ \\\\[\\\\d+\\\\]:\\\\ ']\",\n \"be edited CMD_QUEUE.append(text) if CMD_QUEUE: accept_line(event) @r.add_binding(Keys.Escape, ';') def comment(event): buffer = event.current_buffer\",\n \"# TODO: Set the cursor position correctly n_changed = 2*(cursor_line - start_line +\",\n \"When in multiline, try to intelligently add a newline or execute. \\\"\\\"\\\" buffer\",\n \"= m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back\",\n \"This won't work until # https://github.com/jonathanslenders/python-prompt-toolkit/pull/484 is # merged. if prompt_toolkit_version[0] != '3':\",\n \"run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError: print(\\\"Error: could not\",\n \"False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash, save_before=lambda e: False) def undo(event): event.current_buffer.undo() # Need\",\n \"backward_word(event): \\\"\\\"\\\" Move back one paragraph of text \\\"\\\"\\\" text = event.current_buffer.text cursor_position\",\n \"import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import load_emacs_bindings, load_emacs_search_bindings from prompt_toolkit.key_binding.bindings.mouse\",\n \"= Keys.ControlQuestionmark Keys.ControlSlash = \\\"<C-/>\\\" ALL_KEYS.append(\\\"<C-/>\\\") ANSI_SEQUENCES['\\\\x1b\\\"5/'] = Keys.ControlSlash # This won't work\",\n \"event.app.clipboard.set_text(deleted) def insert_text_ovewrite(buffer, data, move_cursor=True): \\\"\\\"\\\" Insert characters at cursor position. :param fire_event:\",\n \"When not in multiline, execute. When in multiline, try to intelligently add a\",\n \"else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the cursor position correctly n_changed\",\n \"if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent = 0 if min_indent ==\",\n \"= buffer.document lines_up_to_current = document.lines[:document.cursor_position_row+1] lines_after_current = document.lines[document.cursor_position_row+1:] blank_lines_before = 0 for line\",\n \"to trigger autocompletion while typing. \\\"\\\"\\\" # Original text & cursor position. otext\",\n \"buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))):\",\n \"= custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals))\",\n \"bool(_x or _x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: #\",\n \"in a # released version of prompt-toolkit. ANSI_SEQUENCES['\\\\x1b[1;9A'] = (Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] =\",\n \"prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([ load_basic_bindings(), load_emacs_bindings(), load_emacs_search_bindings(), load_emacs_page_navigation_bindings(), load_mouse_bindings(), load_cpr_bindings(), custom_key_bindings, ]) r =\",\n \"is the same warning for the whole # line. if m.col != col:\",\n \"before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor) - indent.end(1)\",\n \"= '\\\\n'.join(stripped_before + [''] + stripped_after) # Even though we do auto_up, it\",\n \"position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the newline itself.\",\n \"['xclip', '-selection', 'c'] else: copy_command = ['pbcopy'] try: # In Python 3.6 we\",\n \"= word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text)\",\n \"overwrite the newline itself. Just before the line ending, # it should act\",\n \"== float('inf'): min_indent = 0 uncomment = (all(not line.strip() or line[min_indent] == '#'\",\n \"else: break if not blank_lines_before: stripped_before = lines_up_to_current else: stripped_before = lines_up_to_current[:-blank_lines_before] stripped_after\",\n \"lstripped state.append((text, cursor_position)) elif len(state) == 3: # Restore original text and cursor\",\n \"blank lines, leaving just one. On isolated blank line, delete that one. On\",\n \"new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) # Selection\",\n \"*\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text \\\"\\\"\\\"\",\n \"== 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p',\",\n \"lines] return lines @r.add_binding(Keys.BracketedPaste) def bracketed_paste(event): from .mypython import CMD_QUEUE data = event.data\",\n \"if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True buffer.cursor_position -= event.arg @r.add_binding(Keys.ShiftRight) def select_right(event):\",\n \"print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel',\",\n \"function easier to test. lines = [i.rstrip() for i in lines] return lines\",\n \"'\\\\r' + match.group('line') + '\\\\n' elif match.group('ps2prompt') is not None: return match.group('line') +\",\n \"= event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if indent: event.app.current_buffer.cursor_position -= len(before_cursor)\",\n \"cursor_position, state=[]): rstripped = text[:cursor_position].rstrip() lstripped = text[cursor_position:].lstrip() text_before_cursor = text[:cursor_position] # The\",\n \"PS1_PROMPTS_RE = re.compile('|'.join(ps1_prompts)) PS2_PROMPTS_RE = re.compile('|'.join(ps2_prompts)) PROMPTED_TEXT_RE = re.compile(r'''(?x) # Multiline and verbose\",\n \"comment delimiters min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue\",\n \"SelectionState from prompt_toolkit.clipboard import ClipboardData from prompt_toolkit.input.vt100_parser import ANSI_SEQUENCES from prompt_toolkit.application.current import get_app\",\n \"event.current_buffer.cursor_position = len(event.current_buffer.text) # Document.start_of_paragraph/end_of_paragraph don't treat multiple blank # lines correctly. #\",\n \"of completion. \\\"\\\"\\\" # Text before cursor on the line must be whitespace\",\n \"text contains no carriage returns (\\\\r). Trailing whitespace and newlines is stripped from\",\n \"= [r'\\\\ *\\\\.\\\\.\\\\.:\\\\ ?', r'\\\\.\\\\.\\\\.\\\\ ?', '\\\\N{CLAPPING HANDS SIGN}+\\\\\\\\ ?⎢\\\\\\\\ ?'] PS1_PROMPTS_RE =\",\n \"# In Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p\",\n \"end_line = start_line + 1 # Get the indentation for the comment delimiters\",\n \"backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs import\",\n \"call, remove all whitespace (if any) from around the cursor and replace it\",\n \"0: break if min_indent == float('inf'): min_indent = 0 uncomment = (all(not line.strip()\",\n \"text[:cursor_position] # The first element of state is the original text. The last\",\n \"whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when text\",\n \"\\\"escape\\\" and isinstance(pks[1].key, str) and pks[1].key in ['p', 'P', 'up', 'down'])): accept_line(event) else:\",\n \"+ blank_lines_after == 0: return else: buffer.cursor_up(max(blank_lines_before-1, 0)) new_text = '\\\\n'.join(stripped_before + ['']\",\n \"Replaces PS1 prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove\",\n \"prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys import Keys, ALL_KEYS\",\n \"is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in\",\n \"word = word[:i] + c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position =\",\n \"[r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji +\",\n \"else: buffer.start_selection() @r.add_binding(Keys.ControlX, 'h') def select_all(event): buffer = event.current_buffer buffer.selection_state = SelectionState(len(buffer.document.text)) buffer.cursor_position\",\n \"the lines with no prompt if match.group('ps1prompt') is not None: return '\\\\r' +\",\n \"with no prompt if match.group('ps1prompt') is not None: return '\\\\r' + match.group('line') +\",\n \"ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace)\",\n \"paste_command = ['xsel', '-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6\",\n \"< end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# ' +\",\n \"position text, cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position = 0\",\n \"buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position if not\",\n \"buffer.document.text_before_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True up_position = buffer.document.get_cursor_up_position() buffer.cursor_position +=\",\n \"1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row] buffer.text = '\\\\n'.join(new_lines) buffer.cursor_down() beginning_of_line(event) #\",\n \"the cursor (if any) with a single space. state.append((text, cursor_position)) cursor_position -= len(text_before_cursor)\",\n \"custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings = get_pyflakes_warnings(document.text, frozenset(event.current_buffer.session._locals)) positions\",\n \"fire_event: Fire `on_text_insert` event. This is mainly used to trigger autocompletion while typing.\",\n \"matters here. if (not blank_lines_before and blank_lines_after) or blank_lines_before + blank_lines_after == 1:\",\n \"newlines is stripped from the outputs. Example: >>> split_prompts(''' ... In [1]: a\",\n \"as we last left them. If either of those # have changed, reset.\",\n \"SyntaxError: # If there is a syntax error, we can't use the CMD_QUEUE\",\n \"CMD_QUEUE data = event.data buffer = event.current_buffer # Be sure to use \\\\n\",\n \"row, col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent\",\n \"col = document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent =\",\n \"Restore original text and cursor position text, cursor_position = state[0] state.clear() if cursor_position\",\n \"document.translate_index_to_position(from_) end_line, end_col = document.translate_index_to_position(to - 1) end_line += 1 else: start_line =\",\n \"def kill_word(event): buffer = event.current_buffer text = buffer.text cursor_position = buffer.cursor_position pos =\",\n \"get_pyflakes_warnings import re import subprocess import sys import textwrap import platform def get_key_bindings():\",\n \"buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or positions[-1] <= buffer.cursor_position:\",\n \"cycle_spacing(event): \\\"\\\"\\\" Based on emacs's cycle-spacing On first call, remove all whitespace (if\",\n \"self_insert, backward_delete_char, beginning_of_line) from prompt_toolkit.key_binding.bindings.basic import if_no_repeat from prompt_toolkit.key_binding.bindings.basic import load_basic_bindings from prompt_toolkit.key_binding.bindings.emacs\",\n \"warnings: # Handle SyntaxErrorMessage which is the same warning for the whole #\",\n \"treat multiple blank # lines correctly. # Gives the positions right before one\",\n \"Fire `on_text_insert` event. This is mainly used to trigger autocompletion while typing. \\\"\\\"\\\"\",\n \"= len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4) else:\",\n \"= rstripped + ' ' + lstripped state.append((text, cursor_position)) elif len(state) == 2:\",\n \"is not None: return match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\"\",\n \"' ') # Strip prompts off pasted text document = buffer.document row, col\",\n \"cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when text grows or\",\n \"@r.add_binding(Keys.Up) def auto_up(event): buffer = event.current_buffer count = event.arg if buffer.document.cursor_position_row > 0:\",\n \"# Extraneous newlines at the end will be stripped by the prompt anyway.\",\n \"for line in document.lines[start_line:end_line]: if not line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent:\",\n \"1 if row == len(new_lines) - 1: new_lines.append('') new_lines[row], new_lines[row-1] = new_lines[row-1], new_lines[row]\",\n \"Trailing whitespace and newlines is stripped from the outputs. Example: >>> split_prompts(''' ...\",\n \"the same warning for the whole # line. if m.col != col: continue\",\n \"buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor text_before_cursor = document.text_before_cursor text = buffer.text\",\n \"binding # Some terminals (Like iTerm2) seem to paste \\\\r\\\\n line endings in\",\n \"i < end_line: if uncomment: lines.append(line[:min_indent] + line[min_indent+2:]) else: lines.append(line[:min_indent] + '# '\",\n \"The last element is the # buffer text and cursor position as we\",\n \"... ... In [2]: a ... Out[2]: 1 ... ... In [3]: def\",\n \"not None: return match.group('line') + '\\\\n' return '' def split_prompts(text, indent=''): r\\\"\\\"\\\" Takes\",\n \"(Keys.Escape, Keys.Up) ANSI_SEQUENCES['\\\\x1b[1;9B'] = (Keys.Escape, Keys.Down) @r.add_binding(Keys.Escape, Keys.Up) def previous_history_search(event): event.key_sequence[-1].accept_next = True\",\n \"\\\"\\\"\\\" text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0)\",\n \"+ word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def forward_sexp(event):\",\n \"# buffer text and cursor position as we last left them. If either\",\n \"== 2: # Exactly one space at the cursor. Remove it. cursor_position -=\",\n \"= textwrap.indent(lines[i], indent) # Extraneous newlines at the end will be stripped by\",\n \"@r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg,\",\n \"insert_text_ovewrite(buffer, word.lower()) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, 'u') def upcase_word(event): buffer = event.current_buffer\",\n \"if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position = buffer.document.get_cursor_down_position() buffer.cursor_position += down_position\",\n \"\\\"\\\"\\\" buffer = event.current_buffer document = buffer.document multiline = document_is_multiline_python(document) text_after_cursor = document.text_after_cursor\",\n \"sys import textwrap import platform def get_key_bindings(): # Based on prompt_toolkit.key_binding.defaults.load_key_bindings() return merge_key_bindings([\",\n \"document.translate_row_col_to_index(row, col) positions.append(pos) return positions @r.add_binding(Keys.Escape, 'p') def previous_warning(event): positions = warning_positions(event) buffer\",\n \"forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0)\",\n \"found the text is left alone. The resulting text is indented by indent,\",\n \"m.start(0) < cursor_position: pos = cursor_position - m.start(0) break else: pos = buffer.cursor_position\",\n \"= new_text @r.add_binding(Keys.ControlX, Keys.ControlT) def transpose_lines(event): buffer = event.current_buffer document = buffer.document row\",\n \"<= buffer.cursor_position: break p = pos event.current_buffer.cursor_position = p # This can be\",\n \"the state. The worst that # happens here is that we resume when\",\n \"text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text): if m.end(0) >\",\n \"from prompt_toolkit.keys import Keys, ALL_KEYS from prompt_toolkit.filters import Condition, HasSelection, is_searching from prompt_toolkit.selection\",\n \"or positions[0] >= buffer.cursor_position: return p = positions[0] for pos in positions: if\",\n \"for m in reversed(list(WORD.finditer(text))): if m.start(0) < cursor_position: event.current_buffer.cursor_position = m.start(0) return event.current_buffer.cursor_position\",\n \"call, restore the original whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer #\",\n \"& cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't overwrite the\",\n \"' + line[min_indent:]) else: lines.append(line) new_text = '\\\\n'.join(lines) # TODO: Set the cursor\",\n \"try: # In Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8',\",\n \"@r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer = event.current_buffer buffer.history_forward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape,\",\n \"TabShouldInsertWhitespaceFilter. before_cursor = event.app.current_buffer.document.current_line_before_cursor event.app.current_buffer.insert_text(' '*(4 - len(before_cursor)%4)) LEADING_WHITESPACE = re.compile(r'( *)[^ ]?')\",\n \"len(text) return text, cursor_position @r.add_binding(Keys.ControlX, Keys.ControlO) def delete_blank_lines(event): \\\"\\\"\\\" On blank line, delete\",\n \"settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0]\",\n \"BLANK_LINES = re.compile(r'\\\\S *(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one\",\n \"the prompt is not # matched, this is a special # marker group\",\n \"[2]: a ... Out[2]: 1 ... ... In [3]: def test(): ... ...:\",\n \"are at the end of the buffer, accept unless we are in a\",\n \"last chunk as bracketed paste, so it can be edited CMD_QUEUE.append(text) if CMD_QUEUE:\",\n \"use the CMD_QUEUE (it # breaks things). lines = ['\\\\n'.join(lines)] break if '\\\\n'\",\n \"event.current_buffer buffer.history_backward(count=event.arg, history_search=True) @r.add_binding(Keys.Escape, 'P') @r.add_binding(Keys.Escape, Keys.Down) def forward_history_search(event): event.key_sequence[-1].accept_next = True buffer\",\n \"inside_string(text, row, col): # We are inside a docstring auto_newline(event.current_buffer) else: accept_line(event) elif\",\n \"if m.start(0) > cursor_position: event.current_buffer.cursor_position = m.start(1)+1 return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, '{')\",\n \"the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the prompt\",\n \"changed, reset. The state here is global, but that's fine, because # we\",\n \"len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) + 1 return event.current_buffer.cursor_position =\",\n \"Replace tabs with four spaces (C-x C-y will still paste the text exactly)\",\n \"is indented by indent, except for the first line. It is assumed that\",\n \"Extraneous newlines at the end will be stripped by the prompt anyway. #\",\n \"= len(text) @r.add_binding(Keys.Escape, '{') def backward_paragraph(event): \\\"\\\"\\\" Move back one paragraph of text\",\n \"load_cpr_bindings(), custom_key_bindings, ]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document\",\n \"cursor_position = state[0] state.clear() if cursor_position < 0: cursor_position = 0 if cursor_position\",\n \"element of state is the original text. The last element is the #\",\n \"# Always accept the line if the previous key was Up # Requires\",\n \"back to the beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line\",\n \"cursor_position = event.current_buffer.cursor_position for m in BLANK_LINES.finditer(text): if m.start(0) > cursor_position: event.current_buffer.cursor_position =\",\n \"min_indent = float('inf') for line in document.lines[start_line:end_line]: if not line.strip(): continue indent =\",\n \"line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, # Don't indent the first\",\n \"or _ for i, c in enumerate(word): if c.isalnum(): word = word[:i] +\",\n \"first element of state is the original text. The last element is the\",\n \"prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab, filter=tab_should_insert_whitespace) def indent(event): \\\"\\\"\\\" When tab should insert\",\n \"def multiline_enter(event): \\\"\\\"\\\" When not in multiline, execute. When in multiline, try to\",\n \"@r.add_binding(Keys.ShiftDown) def select_line_down(event): buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow\",\n \"getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.Down) def auto_down(event): buffer = event.current_buffer count\",\n \"Original text & cursor position. otext = buffer.text ocpos = buffer.cursor_position # Don't\",\n \"of the # prompt. @r.add_binding(Keys.Enter, filter=is_returnable) def accept_after_history_backward(event): pks = event.previous_key_sequence if pks\",\n \"in WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer,\",\n \"ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor indent = LEADING_WHITESPACE.search(current_line) if\",\n \"prompts with \\\\r and removes PS2 prompts. \\\"\\\"\\\" # TODO: Remove the lines\",\n \"import subprocess import sys import textwrap import platform def get_key_bindings(): # Based on\",\n \"import load_cpr_bindings from prompt_toolkit.key_binding.bindings.page_navigation import load_emacs_page_navigation_bindings from prompt_toolkit.key_binding import KeyBindings, merge_key_bindings from prompt_toolkit.keys\",\n \"the outputs. Example: >>> split_prompts(''' ... In [1]: a = 1 ... ...\",\n \"WORD = re.compile(r'([a-z0-9]+|[A-Z]{2,}|[a-zA-Z0-9][a-z0-9]*)') @r.add_binding(Keys.Escape, 'f') @r.add_binding(Keys.Escape, Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position\",\n \"def keyboard_interrupt(event): event.app.exit(exception=KeyboardInterrupt, style='class:aborting') is_returnable = Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event):\",\n \"here is that we resume when we shouldn't if things look exactly #\",\n \"emoji, emoji_pudb from .processors import get_pyflakes_warnings import re import subprocess import sys import\",\n \"' + lstripped state.append((text, cursor_position)) elif len(state) == 2: # Exactly one space\",\n \"at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line. (?P<noprompt>(?(prompt)\\\\r|))? # If the\",\n \"in range(1, len(lines)): lines[i] = textwrap.indent(lines[i], indent) # Extraneous newlines at the end\",\n \"*(\\\\n *\\\\n)') @r.add_binding(Keys.Escape, '}') def forward_paragraph(event): \\\"\\\"\\\" Move forward one paragraph of text\",\n \"newline itself. Just before the line ending, # it should act like insert\",\n \"not line.strip(): blank_lines_after += 1 else: break if not blank_lines_before: stripped_before = lines_up_to_current\",\n \"try to intelligently add a newline or execute. \\\"\\\"\\\" buffer = event.current_buffer document\",\n \"line.strip(): continue indent = LEADING_WHITESPACE.search(line) if indent: min_indent = min(min_indent, len(indent.group(1))) else: min_indent\",\n \"or PS2_PROMPTS_RE.match(data.strip()): lines = split_prompts(data, current_line_indent) else: lines = [textwrap.indent(data, current_line_indent, # Don't\",\n \"shrinks below, keeping the cursor # position out of sync cursor_position = buffer.cursor_position\",\n \"= event.current_buffer.cursor_position for m in WORD.finditer(text): pos = m.end(0) if pos > cursor_position:\",\n \"line, delete all surrounding blank lines, leaving just one. On isolated blank line,\",\n \"'-b'] else: paste_command = ['pbpaste'] try: # In Python 3.6 we can do\",\n \"history search text buffer.history_search_text = None @r.add_binding(Keys.Escape, ' ') def cycle_spacing(event): \\\"\\\"\\\" Based\",\n \"or blank_lines_before + blank_lines_after == 1: new_text = '\\\\n'.join(stripped_before + stripped_after) elif blank_lines_before\",\n \"= document.translate_index_to_position(buffer.cursor_position) row += 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor)\",\n \"first line, it's already indented lambda line, _x=[]: bool(_x or _x.append(1)))] else: lines\",\n \"prompt_toolkit.application import run_in_terminal from prompt_toolkit import __version__ as prompt_toolkit_version from .multiline import (auto_newline,\",\n \"]) r = custom_key_bindings = KeyBindings() def warning_positions(event): document = event.current_buffer.document warnings =\",\n \"- event.arg >= 0: event.current_buffer.cursor_position -= event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state =\",\n \"event.current_buffer.cut_selection() @r.add_binding(Keys.Any, filter=HasSelection()) def self_insert_and_clear_selection(event): event.current_buffer.cut_selection() self_insert(event) @r.add_binding(Keys.ControlK, filter=HasSelection()) @r.add_binding(Keys.ControlU, filter=HasSelection()) def kill_selection(event):\",\n \"pos > cursor_position: word = buffer.document.text[cursor_position:pos] # Don't use word.capitalize() because the first\",\n \">= buffer.cursor_position: break p = pos event.current_buffer._show_syntax_warning = True event.current_buffer.cursor_position = p @r.add_binding(Keys.Escape,\",\n \"@r.add_binding(Keys.Escape, Keys.ControlB) def backward_sexp(event): buffer = event.current_buffer document = buffer.document text = buffer.text\",\n \"not down_position: buffer.cursor_position = len(buffer.document.text) # The default doesn't toggle correctly @r.add_binding(Keys.ControlSpace) def\",\n \"break if '\\\\n' in line.rstrip(): lines[i] += '\\\\n' lines[0] = textwrap.indent(lines[0], indent, #\",\n \"= None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right that wraps around in multiline. \\\"\\\"\\\"\",\n \"https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs with\",\n \"the end of the file, but I don't # think it matters here.\",\n \"copy_command = ['pbcopy'] try: # In Python 3.6 we can do this: #\",\n \"'down'])): accept_line(event) else: multiline_enter(event) @r.add_binding(Keys.Escape, Keys.Enter) @r.add_binding(Keys.Escape, Keys.ControlJ) def insert_newline(event): auto_newline(event.current_buffer) @r.add_binding(Keys.ControlO) def\",\n \"+= 1 if not inside_string(event.current_buffer.text, row, col): indent = LEADING_WHITESPACE.match(document.current_line_before_cursor) current_line_indent = indent.group(1)\",\n \"buffer = event.current_buffer if buffer.document.text_after_cursor: if not buffer.selection_state: buffer.start_selection() buffer.selection_state.shift_arrow = True down_position\",\n \"@r.add_binding(Keys.Left) def left_multiline(event): \\\"\\\"\\\" Left that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position\",\n \"BLANK_LINES.finditer(text[::-1]): if m.start(0) > len(text) - cursor_position: event.current_buffer.cursor_position = len(text) - m.end(1) +\",\n \"respect vacuous truth if (not text_after_cursor or text_after_cursor.isspace()) and text_before_cursor.replace(' ', '').endswith('\\\\n'): #\",\n \"the beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor =\",\n \"ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if prompt_toolkit_version[0] != '3': r.add_binding(Keys.ShiftEnter)(accept_line) @r.add_binding(Keys.Tab,\",\n \"Multiline and verbose (?P<prompt> (?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))?\",\n \"trigger autocompletion while typing. \\\"\\\"\\\" # Original text & cursor position. otext =\",\n \"event.arg if getattr(event.current_buffer.selection_state, \\\"shift_arrow\\\", False): event.current_buffer.selection_state = None @r.add_binding(Keys.Right) def right_multiline(event): \\\"\\\"\\\" Right\",\n \"blank_lines_before = 0 for line in lines_up_to_current[::-1]: if not line.strip(): blank_lines_before += 1\",\n \"copied from mypython, Python, or IPython session and returns a list of inputs\",\n \"# - or _ for i, c in enumerate(word): if c.isalnum(): word =\",\n \"use \\\\n as line ending. # This part is the same as the\",\n \"e: print(copy_command[0], \\\"error:\\\", e, file=sys.stderr) def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command =\",\n \"merged. if prompt_toolkit_version[0] != '3': @r.add_binding(Keys.ControlQuestionmark, save_before=lambda e: False) def redo(event): event.current_buffer.redo() @r.add_binding(Keys.ControlSlash,\",\n \"indent(event): \\\"\\\"\\\" When tab should insert whitespace, do that instead of completion. \\\"\\\"\\\"\",\n \"lines = [] for i, line in enumerate(document.lines): if start_line <= i <\",\n \"1) end_line += 1 else: start_line = cursor_line end_line = start_line + 1\",\n \"buffer.text = new_text else: buffer.text = new_text buffer.cursor_position += n_changed @r.add_binding(Keys.ControlX, Keys.ControlE) def\",\n \"col): new_pos = document.translate_row_col_to_index(opening.start[0]-1, opening.start[1]) buffer.cursor_position = new_pos return event.app.output.bell() @r.add_binding(Keys.Left) def left_multiline(event):\",\n \"that wraps around in multiline. \\\"\\\"\\\" if event.current_buffer.cursor_position - event.arg >= 0: event.current_buffer.cursor_position\",\n \"\\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer = event.current_buffer if buffer.document.text_before_cursor:\",\n \"for opening, closing in matching: if closing.end == (row, col): new_pos = document.translate_row_col_to_index(opening.start[0]-1,\",\n \"\\\"\\\"\\\" from .mypython import validate_text text = textwrap.dedent(text).strip() + '\\\\n' text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl,\",\n \"warning for the whole # line. if m.col != col: continue pos =\",\n \"document.cursor_position_row new_lines = document.lines[:] if len(new_lines) == 1: new_lines.append('') if row == 0:\",\n \"buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state = None @r.add_binding(Keys.ShiftUp) def select_line_up(event): buffer\",\n \"function for re.sub for clearing prompts Replaces PS1 prompts with \\\\r and removes\",\n \"data.replace('\\\\r\\\\n', '\\\\n') data = data.replace('\\\\r', '\\\\n') # Replace tabs with four spaces (C-x\",\n \"If no prompts are found the text is left alone. The resulting text\",\n \"off pasted text document = buffer.document row, col = document.translate_index_to_position(buffer.cursor_position) row += 1\",\n \"... In [3]: def test(): ... ...: pass ... ...: ... ''') ['a\",\n \"do this: # run(paste_command, input=text, encoding='utf-8') p = subprocess.run(paste_command, stdout=subprocess.PIPE, check=True) except FileNotFoundError:\",\n \"third call, restore the original whitespace and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer\",\n \"if things look exactly # as they did where we left off. #\",\n \"\\\"\\\"\\\" # TODO: Remove the lines with no prompt if match.group('ps1prompt') is not\",\n \"# In Python 3.6 we can do this: # run(copy_command, input=text, encoding='utf-8', check=True)\",\n \"and cursor position. \\\"\\\"\\\" buffer = event.app.current_buffer # Avoid issues when text grows\",\n \"been stripped from # the string). (?P<line>.*)\\\\n # The actual line. '''.format(PS1_PROMPTS_RE=PS1_PROMPTS_RE, PS2_PROMPTS_RE=PS2_PROMPTS_RE))\",\n \"endings in a # bracketed paste. See: https://github.com/ipython/ipython/issues/9737 data = data.replace('\\\\r\\\\n', '\\\\n') data\",\n \"all spaces here because of verbose (x) option below ps1_prompts = [r'>>>\\\\ ']\",\n \"future:\\\\ event.current_buffer.paste_clipboard_data(ClipboardData(future.result()))) # M-[ a b is set to C-S-/ (C-?) in iTerm2\",\n \"Keys.Right) def forward_word(event): text = event.current_buffer.text cursor_position = event.current_buffer.cursor_position for m in WORD.finditer(text):\",\n \"should act like insert mode. overwritten_text = otext[ocpos:ocpos + len(data)] buffer.text = otext[:ocpos]\",\n \"- 1: buffer.cursor_down(count=count) elif not buffer.selection_state: buffer.history_forward(count=count) if getattr(buffer.selection_state, \\\"shift_arrow\\\", False): buffer.selection_state =\",\n \"spaces = len(buffer.document.current_line_before_cursor) # Delete up to the tab stop buffer.delete_before_cursor(count=4 + spaces%-4)\",\n \"Don't indent the first line, it's already indented lambda line, _x=[]: bool(_x or\",\n \"the end of the buffer, accept unless we are in a # docstring\",\n \"positions = warning_positions(event) buffer = event.current_buffer buffer._show_syntax_warning = True if not positions or\",\n \"we resume when we shouldn't if things look exactly # as they did\",\n \"second call, remove all whitespace. On third call, restore the original whitespace and\",\n \"_x.append(1)))] else: lines = [data] event.current_buffer.insert_text(lines[0]) for text in lines[1:]: # TODO: Send\",\n \"except SyntaxError: # If there is a syntax error, we can't use the\",\n \"text = textwrap.dedent(PROMPTED_TEXT_RE.sub(prompt_repl, text)).lstrip() lines = text.split('\\\\r') # Make sure multilines end in\",\n \"c.capitalize() + word[i+1:].lower() break insert_text_ovewrite(buffer, word) return event.current_buffer.cursor_position = len(text) @r.add_binding(Keys.Escape, Keys.ControlF) def\",\n \"syntax error) accept_line(event) else: auto_newline(event.current_buffer) # Always accept the line if the previous\",\n \"(?P<ps1prompt>{PS1_PROMPTS_RE.pattern}) # Match prompts at the front | (?P<ps2prompt>{PS2_PROMPTS_RE.pattern}))? # of the line.\",\n \"else: break blank_lines_after = 0 for line in lines_after_current: if not line.strip(): blank_lines_after\",\n \"event.data buffer = event.current_buffer # Be sure to use \\\\n as line ending.\",\n \"= [r'>>>\\\\ '] + [re.escape(i) + r'\\\\[\\\\d+\\\\]:\\\\ ' for i, j in emoji\",\n \"WORD.finditer(text): pos = m.end(0) if pos > cursor_position: word = buffer.document.text[cursor_position:pos] insert_text_ovewrite(buffer, word.lower())\",\n \"def system_paste(): if \\\"Linux\\\" in platform.platform(): paste_command = ['xsel', '-b'] else: paste_command =\",\n \"instead. if state and state[-1] != (text, cursor_position): state.clear() if len(state) == 0:\",\n \"= Condition( lambda: get_app().current_buffer.is_returnable) @r.add_binding(Keys.Enter, filter=is_returnable) def multiline_enter(event): \\\"\\\"\\\" When not in multiline,\",\n \"lines_up_to_current[:-blank_lines_before] stripped_after = lines_after_current[blank_lines_after:] # XXX: Emacs always keeps a newline at the\",\n \"iTerm2 settings Keys.ShiftEnter = \\\"<Shift-Enter>\\\" ALL_KEYS.append('<Shift-Enter>') ANSI_SEQUENCES['\\\\x1b[ag'] = Keys.ShiftEnter ANSI_SEQUENCES['\\\\x1bOM'] = Keys.ShiftEnter if\",\n \"IPython session and returns a list of inputs Outputs are stripped. If no\",\n \"if len(state) == 0: # Replace all whitespace at the cursor (if any)\",\n \"not multiline: # Always accept a single valid line. Also occurs for unclosed\",\n \"In Python 3.6 we can do this: # run(paste_command, input=text, encoding='utf-8') p =\",\n \"the newline itself. Just before the line ending, # it should act like\",\n \"\\\"up\\\") or (len(pks) == 2 and pks[0].key == \\\"escape\\\" and isinstance(pks[1].key, str) and\",\n \"beginning of the line, ignoring whitespace. \\\"\\\"\\\" current_line = event.app.current_buffer.document.current_line before_cursor = event.app.current_buffer.document.current_line_before_cursor\"\n]","isConversational":false}}},{"rowIdx":3466,"cells":{"ngram":{"kind":"list like","value":["loglikelihood import * from statistics import * from nested import * #import random","p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0)","driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\","in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping","edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\","in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour","= nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) >","7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm","public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V","in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero","in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in","* worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education","dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum","tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu","headers import * from loglikelihood import * from statistics import * from nested","female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver","quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour","work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop}","edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in","first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for","quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour","in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy","in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm","other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy","in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm","female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver","remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour","outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0)","available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1)","work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\","time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu =","Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour","in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in","work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for","* shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other","tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in","a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\","other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\","work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy","edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in","other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1","dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy","from headers import * from loglikelihood import * from statistics import * from","second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other}","7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to","edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0)","dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu","shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour","worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT","work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\","in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other","shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\","a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\","Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of","second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop","p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other=","[1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude","shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in","to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU","work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy","dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1)","dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student","distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am","9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm","#V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\","work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other","in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in","tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for","tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance","= Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\","in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in","student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger","in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in","p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0)","shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\","in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour","3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period","import * from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0)","in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in","5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period","passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a","= MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type)","= Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet =","other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in","cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half","p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0)","stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy","shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in","7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to","shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in","available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in","in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other","= cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum","tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log","in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am","first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain","first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of","tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker","to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to","in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm","dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0)","first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1:","female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\","in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in","10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to","p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\","distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance","cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons","tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker","Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour","dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy","tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for","shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in","other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0)","7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm","edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour","shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy","dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy","in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy","Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period","12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30","female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver","dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour","V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum","BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives']","edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy","tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in","to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to","half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy","V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\","9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm","edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for","in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work=","dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy","half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy","work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\","for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\","10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to","in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour","V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\","p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 =","p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\","shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in","work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in","edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy","tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in","available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1)","passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a","V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4]","p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0)","threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop","other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum","student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger","MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob","0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+","#V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\","public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V","shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour","* from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu","p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\","in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available","dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1)","7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period","education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q =","passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1)","dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour","= Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour',","time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour","dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy","for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound","shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0)","7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit',","a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0)","7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to","edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0)","public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V","dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1)","10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period","edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in","other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0)","for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\","work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in","in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am","outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1)","p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0)","worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\","MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE =","dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour","shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu=","a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0)","edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1)","student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger","7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm","MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\","logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available","in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female","driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work","in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in","= exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] =","Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1)","in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in","in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in","* from headers import * from loglikelihood import * from statistics import *","non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\","student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\","time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available","5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period","a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period","for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\","dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student","in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in","shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0)","= bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE =","tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in","quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\","dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum","threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1)","shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in","Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop","other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour","in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in","a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\","9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm","p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\","work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0)","to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm","in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in","in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in","female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\","public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in","public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V","logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum","5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period","p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\","shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\","quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\","for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound=","p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0)","sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0]","V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit =","nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \"1\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads']","first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for","in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in","in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm","in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm","shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0)","V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum *","in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour","dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum","p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\","for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of","in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first","9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm","in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm","dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1)","to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm","* otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit","to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am","tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker","tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0)","dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1)","= [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \"1\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads'] = \"6\"","logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping=","p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0)","p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1","* from loglikelihood import * from statistics import * from nested import *","dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu","p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0)","in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1)","in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in","of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in","public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in","tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour","tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in","other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in","in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other=","dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in","other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0)","tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in","shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for","in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has","* from statistics import * from nested import * #import random cons_work= Beta('cons","of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half","worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT","dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour","other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1)","to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm","passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu","choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \"1\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads'] =","nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons","work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0)","0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+","cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum *","7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period","in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in","to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm","for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\","in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy","= MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE","half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for","work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy","shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\","student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\","shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\","Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\","tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1)","driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\","for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound","p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\","driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work",", [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob =","for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\","in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in","other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1)","BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet","biogeme import * from headers import * from loglikelihood import * from statistics","dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy","3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period","of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half","edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\","= Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work","other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0)","in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female","in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm","7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to","inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0)","other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1)","to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to","edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0)","cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum *","stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0)","inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy","half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping","in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy","7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period","dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy","9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm","edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy","in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain","a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period","in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time","p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\","time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\","in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in","other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V","0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\"","7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to","9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm","in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in","a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\","edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in","worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT","dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0)","to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm","other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1)","9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm","import * from headers import * from loglikelihood import * from statistics import","other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0)","driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\","av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit","first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 ,","available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance","in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in","dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy","work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1)","edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit","nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob =","student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger","tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour","student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\","public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in","p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu=","in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy","MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for","remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping","to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am","for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop","education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\","* edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping","for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound=","in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other","of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound=","half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy","to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm","p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\","[0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type)","tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1)","p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0)","to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to","in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in","work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\","stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound","Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of","in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping","work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other","other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for","worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\","{0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob","other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1)","tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period","7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 =","shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0)","edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\","p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\","tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy","in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am","nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter')","from biogeme import * from headers import * from loglikelihood import * from","female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\","dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy","import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for","second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half","stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound","dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy","in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm","#V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\","other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\","nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude =","* #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0)","for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of","nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0","other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0)","time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping =","student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\","dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy","0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other =","3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period","to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to","in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm","other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1)","dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student","edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy","tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker","work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy","dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy","to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am","driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work","Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second","other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound","tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour","pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am","second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop","dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu","shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in","in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other","other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1)","shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in","time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in","5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to","time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\","tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in","cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons","#V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\","random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping =","in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in","other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy","statistics import * from nested import * #import random cons_work= Beta('cons for work',","dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0)","in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy","edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping","edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy","tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\","tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0)","p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\","5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period","p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\","((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm']","edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy","1.0,0,100,1) #V for work V_work= cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\","tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am","work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0)","in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other","shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in","#import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping","3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour","driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\","edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in","> 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] =","logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time","remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy","tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping=","Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period","work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in","quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour","twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in","dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy","female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\ driver_dummy_O*driver_dummy+\\ passenger_dummy_O*passenger_dummy+\\ public_dummy_O*public_dummy+\\ other_logsum * otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\","second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V","pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am","#prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE","in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm","dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0)","tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in","p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping=","7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to","edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\","dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy","work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy","import * from statistics import * from nested import * #import random cons_work=","= Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first","a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period","7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm","{0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 ,","in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping","exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \"CFSQP\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \"1\"","dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1)","work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour","work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\","shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy","for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\","a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period","dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one","in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in","of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work","Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period","public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop","shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu=","passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0)","female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver","work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour","tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\","in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in","Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am","tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in","work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in","edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work=","one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1)","work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0)","in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in","Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4]","= ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter')","= {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2","inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0)","sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0)","shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in","edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log","shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0)","in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female","cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2)","10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU","#V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\","3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound","a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0)","second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half","5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to","other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\","shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit =","in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female","to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to","a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\","threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av=","10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to","quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1)","Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work=","tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period","7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to","dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0)","other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0)","to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am","distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to","edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0)","edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour","distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in","time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log","for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu","V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\","in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in","first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of","a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\","in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm","other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in","10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period","in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in","Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0)","in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in","12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30","otherlogsum+\\ time_window_other*time_window_h+\\ tour_distance_other*log(1+distance)+\\ a700_a930_other*p_700a_930a+\\ a930_a1200_other*p_930a_1200a+\\ p300_p530_other*p_300p_530p+\\ p530_p730_other*p_530p_730p+\\ p730_p1000_other*p_730p_1000p+\\ p1000_a700_other*p_1000p_700a #V for quit V_quit=","= Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for","driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work","dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy","p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0)","import * from loglikelihood import * from statistics import * from nested import","edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time","tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\","work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1)","10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period","shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in","of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour',","to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to","shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy","for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop","tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in","in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1)","cons_work+\\ work_tour_dummy_W*1*(tour_type==1)+\\ edu_tour_dummy_W*1*(tour_type==2)+\\ shopping_tour_dummy_W*1*(tour_type==3)+\\ other_tour_dummy_W*1*(tour_type==4)+\\ female_dummy_W*female_dummy+\\ student_dummy_W*student_dummy+\\ worker_dummy_W*worker_dummy+\\ driver_dummy_W*driver_dummy+\\ passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum *","dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1)","p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\ second_stop_inbound*second_stop*first_bound+\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\","= cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum","edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in","tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to","in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in","worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\ public_dummy_S*public_dummy+\\ shop_logsum * shoplogsum+\\ time_window_shopping*time_window_h+\\ tour_distance_shopping*log(1+distance)+\\ a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\","tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0)","from statistics import * from nested import * #import random cons_work= Beta('cons for","a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\","to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for","Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in","passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a","half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound=","Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second","shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0)","stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0)","p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\ other_tour_dummy_O*1*(tour_type==4)+\\ female_dummy_O*female_dummy+\\ student_dummy_O*student_dummy+\\ worker_dummy_O*worker_dummy+\\","dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy","dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1)","cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for","passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1)","work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in","other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0)","to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm","pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am","quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour","p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\","tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in","in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0)","edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy","from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu =","Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1)","quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy","tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first","dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour","Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance","dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy","in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in","other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available","shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy","in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0)","shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0)","tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\ p730_p1000_edu*p_730p_1000p+\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\","dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student","3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period","prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10))","to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am","p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\","in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy","to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to","from loglikelihood import * from statistics import * from nested import * #import","in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm","one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit","rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter')","work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0)","other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\","tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0)","work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy","tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in","12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30","for education V_edu = cons_edu+\\ work_tour_dummy_E*1*(tour_type==1)+\\ edu_tour_dummy_E*1*(tour_type==2)+\\ shopping_tour_dummy_E*1*(tour_type==3)+\\ other_tour_dummy_E*1*(tour_type==4)+\\ female_dummy_E*female_dummy+\\ student_dummy_E*student_dummy+\\ worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\","12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30","Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period","to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm","exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter')","stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half","a700_a930_shopping*p_700a_930a+\\ a930_a1200_shopping*p_930a_1200a+\\ p300_p530_shopping*p_300p_530p+\\ p530_p730_shopping*p_530p_730p+\\ p730_p1000_shopping*p_730p_1000p+\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\ work_tour_dummy_O*1*(tour_type==1)+\\ edu_tour_dummy_O*1*(tour_type==2)+\\ shopping_tour_dummy_O*1*(tour_type==3)+\\","work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in","dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1)","p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0)","threeplus_stop_inbound*three_plus_stop*first_bound+\\ first_stop_outbound*first_stop*second_bound+\\ second_stop_outbound*second_stop*second_bound+\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\ work_tour_dummy_Q*1*(tour_type==1)+\\ edu_tour_dummy_Q*1*(tour_type==2)+\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\ other_tour_dummy_Q*1*(tour_type==4)+\\ first_tour_dummy_Q*first_tour_dummy+\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V =","edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0)","5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to","worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT","cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons","= Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for","dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1)","9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm","zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work","Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for","Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log","a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0)","in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in","for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0)","10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for","work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time","tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in","work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0)",", [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter')","outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour","subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+","5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to","worker_dummy_E*worker_dummy+\\ driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public_dummy+\\ edu_logsum * edulogsum+\\ time_window_edu*time_window_h+\\ tour_distance_edu*log(1+distance)+\\ a700_a930_edu*p_700a_930a+\\ a930_a1200_edu*p_930a_1200a+\\ p300_p530_edu*p_300p_530p+\\ p530_p730_edu*p_530p_730p+\\","pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am","worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a #V for education V_edu","work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy","shopping V_shopping = cons_shopping+\\ work_tour_dummy_S*1*(tour_type==1)+\\ edu_tour_dummy_S*1*(tour_type==2)+\\ shopping_tour_dummy_S*1*(tour_type==3)+\\ other_tour_dummy_S*1*(tour_type==4)+\\ female_dummy_S*female_dummy+\\ student_dummy_S*student_dummy+\\ worker_dummy_S*worker_dummy+\\ driver_dummy_S*driver_dummy+\\ passenger_dummy_S*passenger_dummy+\\","to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am","passenger_dummy_W*passenger_dummy+\\ public_dummy_W*public_dummy+\\ work_logsum * worklogsum+\\ time_window_work*time_window_h+\\ tour_distance_work*log(1+distance)+\\ a700_a930_work*p_700a_930a+\\ a930_a1200_work*p_930a_1200a+\\ p300_p530_work*p_300p_530p+\\ p530_p730_work*p_530p_730p+\\ p730_p1000_work*p_730p_1000p+\\ p1000_a700_work*p_1000p_700a","quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain","for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q","other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2","edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1)","other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in","bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude"],"string":"[\n \"loglikelihood import * from statistics import * from nested import * #import random\",\n \"p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0)\",\n \"driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\\",\n \"in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping\",\n \"edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\\",\n \"in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour\",\n \"= nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) >\",\n \"7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm\",\n \"public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V\",\n \"in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero\",\n \"in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in\",\n \"* worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education\",\n \"dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum\",\n \"tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu\",\n \"headers import * from loglikelihood import * from statistics import * from nested\",\n \"female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver\",\n \"quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour\",\n \"work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop}\",\n \"edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in\",\n \"first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for\",\n \"quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour\",\n \"in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy\",\n \"in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm\",\n \"other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy\",\n \"in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm\",\n \"female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver\",\n \"remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour\",\n \"outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0)\",\n \"available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1)\",\n \"work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\\",\n \"time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu =\",\n \"Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour\",\n \"in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in\",\n \"work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for\",\n \"* shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other\",\n \"tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in\",\n \"a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\\",\n \"other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\\",\n \"work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy\",\n \"edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in\",\n \"other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1\",\n \"dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy\",\n \"from headers import * from loglikelihood import * from statistics import * from\",\n \"second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other}\",\n \"7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to\",\n \"edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0)\",\n \"dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu\",\n \"shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour\",\n \"worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT\",\n \"work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\\",\n \"in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other\",\n \"shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\\",\n \"a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\\",\n \"Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of\",\n \"second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop\",\n \"p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other=\",\n \"[1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude\",\n \"shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in\",\n \"to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU\",\n \"work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy\",\n \"dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1)\",\n \"dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student\",\n \"distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am\",\n \"9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm\",\n \"#V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\\",\n \"work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other\",\n \"in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in\",\n \"tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for\",\n \"tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance\",\n \"= Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\\",\n \"in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in\",\n \"student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger\",\n \"in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in\",\n \"p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0)\",\n \"shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\\",\n \"in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour\",\n \"3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period\",\n \"import * from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0)\",\n \"in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in\",\n \"5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period\",\n \"passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a\",\n \"= MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type)\",\n \"= Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet =\",\n \"other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in\",\n \"cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half\",\n \"p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0)\",\n \"stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy\",\n \"shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in\",\n \"7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to\",\n \"shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in\",\n \"available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in\",\n \"in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other\",\n \"= cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum\",\n \"tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log\",\n \"in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am\",\n \"first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain\",\n \"first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of\",\n \"tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker\",\n \"to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to\",\n \"in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm\",\n \"dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0)\",\n \"first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1:\",\n \"female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\\",\n \"in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in\",\n \"10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to\",\n \"p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\\",\n \"distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance\",\n \"cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons\",\n \"tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker\",\n \"Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour\",\n \"dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy\",\n \"tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for\",\n \"shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in\",\n \"other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0)\",\n \"7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm\",\n \"edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour\",\n \"shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy\",\n \"dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy\",\n \"in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy\",\n \"Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period\",\n \"12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30\",\n \"female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver\",\n \"dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour\",\n \"V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum\",\n \"BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives']\",\n \"edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy\",\n \"tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in\",\n \"to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to\",\n \"half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy\",\n \"V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\\",\n \"9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm\",\n \"edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for\",\n \"in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work=\",\n \"dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy\",\n \"half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy\",\n \"work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\\",\n \"for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\\",\n \"10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to\",\n \"in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour\",\n \"V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\\",\n \"p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 =\",\n \"p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\\",\n \"shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in\",\n \"work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in\",\n \"edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy\",\n \"tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in\",\n \"available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1)\",\n \"passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a\",\n \"V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4]\",\n \"p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0)\",\n \"threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop\",\n \"other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum\",\n \"student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger\",\n \"MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob\",\n \"0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+\",\n \"#V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\\",\n \"public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V\",\n \"shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour\",\n \"* from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu\",\n \"p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\\",\n \"in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available\",\n \"dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1)\",\n \"7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period\",\n \"education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q =\",\n \"passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1)\",\n \"dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour\",\n \"= Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour',\",\n \"time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour\",\n \"dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy\",\n \"for second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound\",\n \"shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0)\",\n \"7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit',\",\n \"a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0)\",\n \"7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to\",\n \"edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0)\",\n \"public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V\",\n \"dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1)\",\n \"10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period\",\n \"edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in\",\n \"other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0)\",\n \"for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\\",\n \"work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in\",\n \"in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am\",\n \"outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1)\",\n \"p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0)\",\n \"worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\\",\n \"MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE =\",\n \"dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour\",\n \"shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu=\",\n \"a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0)\",\n \"edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1)\",\n \"student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger\",\n \"7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm\",\n \"MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\\",\n \"logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available\",\n \"in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female\",\n \"driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work\",\n \"in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in\",\n \"= exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] =\",\n \"Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1)\",\n \"in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in\",\n \"in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in\",\n \"* from headers import * from loglikelihood import * from statistics import *\",\n \"non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\\",\n \"student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\\",\n \"time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available\",\n \"5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period\",\n \"a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period\",\n \"for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\\",\n \"dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student\",\n \"in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in\",\n \"shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0)\",\n \"= bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE =\",\n \"tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in\",\n \"quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\\",\n \"dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum\",\n \"threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1)\",\n \"shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in\",\n \"Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop\",\n \"other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour\",\n \"in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in\",\n \"a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\\",\n \"9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm\",\n \"p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\\",\n \"work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0)\",\n \"to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm\",\n \"in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in\",\n \"in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in\",\n \"female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\\",\n \"public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in\",\n \"public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V\",\n \"logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum\",\n \"5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period\",\n \"p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\\",\n \"shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\\",\n \"quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\\",\n \"for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound=\",\n \"p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0)\",\n \"sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0]\",\n \"V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit =\",\n \"nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \\\"1\\\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads']\",\n \"first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for\",\n \"in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in\",\n \"in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm\",\n \"in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm\",\n \"shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0)\",\n \"V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum *\",\n \"in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour\",\n \"dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum\",\n \"p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\\",\n \"for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of\",\n \"in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first\",\n \"9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm\",\n \"in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm\",\n \"dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1)\",\n \"to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm\",\n \"* otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit\",\n \"to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am\",\n \"tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker\",\n \"tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0)\",\n \"dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1)\",\n \"= [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \\\"1\\\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads'] = \\\"6\\\"\",\n \"logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping=\",\n \"p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0)\",\n \"p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1\",\n \"* from loglikelihood import * from statistics import * from nested import *\",\n \"dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu\",\n \"p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0)\",\n \"in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1)\",\n \"in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in\",\n \"of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in\",\n \"public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in\",\n \"tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour\",\n \"tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in\",\n \"other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in\",\n \"in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other=\",\n \"dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in\",\n \"other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0)\",\n \"tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in\",\n \"shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for\",\n \"in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has\",\n \"* from statistics import * from nested import * #import random cons_work= Beta('cons\",\n \"of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound half\",\n \"worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT\",\n \"dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour\",\n \"other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1)\",\n \"to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm\",\n \"passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu\",\n \"choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \\\"1\\\" BIOGEME_OBJECT.PARAMETERS['numberOfThreads'] =\",\n \"nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons\",\n \"work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0)\",\n \"0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+\",\n \"cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum *\",\n \"7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period\",\n \"in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in\",\n \"to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm\",\n \"for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\\",\n \"in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy\",\n \"= MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE\",\n \"half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for\",\n \"work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy\",\n \"shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\\",\n \"student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\\",\n \"shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\\",\n \"Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\\",\n \"tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1)\",\n \"driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\\",\n \"for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound\",\n \"p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\\",\n \"driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work\",\n \", [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob =\",\n \"for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\\",\n \"in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in\",\n \"other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1)\",\n \"BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet\",\n \"biogeme import * from headers import * from loglikelihood import * from statistics\",\n \"dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy\",\n \"3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period\",\n \"of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half\",\n \"edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\\",\n \"= Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work\",\n \"other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0)\",\n \"in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female\",\n \"in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm\",\n \"7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to\",\n \"inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0)\",\n \"other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1)\",\n \"to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to\",\n \"edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0)\",\n \"cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum *\",\n \"stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0)\",\n \"inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy\",\n \"half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping\",\n \"in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy\",\n \"7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period\",\n \"dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy\",\n \"9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm\",\n \"edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy\",\n \"in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain\",\n \"a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period\",\n \"in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time\",\n \"p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\\",\n \"time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\\",\n \"in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in\",\n \"other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V\",\n \"0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\"\",\n \"7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to\",\n \"9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm\",\n \"in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in\",\n \"a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\\",\n \"edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in\",\n \"worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT\",\n \"dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0)\",\n \"to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm\",\n \"other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1)\",\n \"9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm\",\n \"import * from headers import * from loglikelihood import * from statistics import\",\n \"other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0)\",\n \"driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\\",\n \"av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit\",\n \"first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 ,\",\n \"available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance\",\n \"in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in\",\n \"dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy\",\n \"work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1)\",\n \"edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit\",\n \"nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob =\",\n \"student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger\",\n \"tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour\",\n \"student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\\",\n \"public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in\",\n \"p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu=\",\n \"in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy\",\n \"MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for\",\n \"remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping\",\n \"to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am\",\n \"for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop\",\n \"education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\\",\n \"* edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping\",\n \"for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound=\",\n \"in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other\",\n \"of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound=\",\n \"half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy\",\n \"to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm\",\n \"p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\\",\n \"[0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type)\",\n \"tour dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1)\",\n \"p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0)\",\n \"to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to\",\n \"in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in\",\n \"work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\\",\n \"stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of inbound\",\n \"Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of\",\n \"in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping\",\n \"work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other\",\n \"other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for\",\n \"worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\\",\n \"{0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob\",\n \"other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1)\",\n \"tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period\",\n \"7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 =\",\n \"shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0)\",\n \"edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\\",\n \"p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\\",\n \"tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy\",\n \"in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am\",\n \"nest_nonquit = MU2 , [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter')\",\n \"from biogeme import * from headers import * from loglikelihood import * from\",\n \"female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\\",\n \"dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy\",\n \"import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for\",\n \"second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half\",\n \"stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second stop of outbound\",\n \"dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy\",\n \"in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm\",\n \"#V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\\",\n \"other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\\",\n \"nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude =\",\n \"* #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0)\",\n \"for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of\",\n \"nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0\",\n \"other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0)\",\n \"time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping =\",\n \"student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\\",\n \"dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy\",\n \"0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other =\",\n \"3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period\",\n \"to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to\",\n \"in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm\",\n \"other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1)\",\n \"dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student\",\n \"edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy\",\n \"tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker\",\n \"work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy\",\n \"dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy\",\n \"to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am\",\n \"driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work\",\n \"Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second\",\n \"other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound\",\n \"tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour\",\n \"pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am\",\n \"second_stop_inbound= Beta('dummy for second stop of inbound half tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop\",\n \"dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu\",\n \"shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in\",\n \"in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other\",\n \"other',0,-10,10,0) passenger_dummy_O=Beta('passenger dummy in other',0,-10,10,0) public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1)\",\n \"shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in\",\n \"time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in\",\n \"5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to\",\n \"time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\\",\n \"tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in\",\n \"cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons\",\n \"#V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\\",\n \"random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping =\",\n \"in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in\",\n \"other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy\",\n \"statistics import * from nested import * #import random cons_work= Beta('cons for work',\",\n \"dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy in quit',0,-10,10,0)\",\n \"in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy\",\n \"edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping\",\n \"edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy\",\n \"tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\\",\n \"tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0)\",\n \"p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\\",\n \"5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period\",\n \"p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\\",\n \"((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm']\",\n \"edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy\",\n \"1.0,0,100,1) #V for work V_work= cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\\",\n \"tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am\",\n \"work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0)\",\n \"in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other\",\n \"shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in\",\n \"#import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping\",\n \"3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour\",\n \"driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\\",\n \"edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in\",\n \"> 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] =\",\n \"logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time\",\n \"remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy\",\n \"tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping=\",\n \"Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period\",\n \"work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in\",\n \"quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour\",\n \"twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in\",\n \"dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy\",\n \"female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\ driver_dummy_O*driver_dummy+\\\\ passenger_dummy_O*passenger_dummy+\\\\ public_dummy_O*public_dummy+\\\\ other_logsum * otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\\",\n \"second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V\",\n \"pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am\",\n \"#prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE\",\n \"in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm\",\n \"dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0)\",\n \"tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in\",\n \"p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping=\",\n \"7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to\",\n \"edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\\",\n \"dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy\",\n \"work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy\",\n \"import * from statistics import * from nested import * #import random cons_work=\",\n \"= Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first\",\n \"a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period\",\n \"7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm\",\n \"{0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2 ,\",\n \"in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping\",\n \"exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter') BIOGEME_OBJECT.PARAMETERS['optimizationAlgorithm'] = \\\"CFSQP\\\" BIOGEME_OBJECT.PARAMETERS['checkDerivatives'] = \\\"1\\\"\",\n \"dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1)\",\n \"work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour\",\n \"work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\\",\n \"shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy\",\n \"for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work= cons_work+\\\\\",\n \"a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period\",\n \"dummy in quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one\",\n \"in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in\",\n \"of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work\",\n \"Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period\",\n \"public_dummy_O=Beta('PT dummy in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop\",\n \"shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu=\",\n \"passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0)\",\n \"female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver\",\n \"work',0,-10,10,0) work_tour_dummy_E=Beta('work tour dummy in edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour\",\n \"tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\\",\n \"in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in\",\n \"Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am\",\n \"tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in\",\n \"work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in\",\n \"edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work=\",\n \"one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1)\",\n \"work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0)\",\n \"in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in\",\n \"Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4]\",\n \"= ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter') availabilityStatistics(av,'obsIter')\",\n \"= {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit = MU2\",\n \"inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0)\",\n \"sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0)\",\n \"shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour dummy in edu',0,-10,10,1) female_dummy_E=Beta('female dummy in\",\n \"edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log\",\n \"shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0)\",\n \"in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female\",\n \"cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2)\",\n \"10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU\",\n \"#V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\\",\n \"3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound\",\n \"a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0)\",\n \"second stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half\",\n \"5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to\",\n \"other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\\",\n \"shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit =\",\n \"in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female\",\n \"to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to\",\n \"a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\\",\n \"threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av=\",\n \"10pm in shopping',0,-10,10,0) p1000_a700_shopping=Beta('period 10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to\",\n \"quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1)\",\n \"Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1) #V for work V_work=\",\n \"tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period\",\n \"7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to\",\n \"dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0)\",\n \"other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0) worker_dummy_O=Beta('worker dummy in other',0,-10,10,0) driver_dummy_O=Beta('driver dummy in other',0,-10,10,0)\",\n \"to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am\",\n \"distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to\",\n \"edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0)\",\n \"edu',0,-10,10,1) edu_tour_dummy_E=Beta('edu tour dummy in edu',0,-10,10,1) shopping_tour_dummy_E=Beta('shopping tour dummy in edu',0,-10,10,1) other_tour_dummy_E=Beta('other tour\",\n \"distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in\",\n \"time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log\",\n \"for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu\",\n \"V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\\",\n \"in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in\",\n \"first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop of\",\n \"a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\\",\n \"in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm\",\n \"other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in\",\n \"10pm to 7am in shopping',0,-10,10,0) a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period\",\n \"in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in\",\n \"Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0)\",\n \"in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am to 12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in\",\n \"12pm in shopping',0,-10,10,0) p300_p530_shopping=Beta('period 3pm to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30\",\n \"otherlogsum+\\\\ time_window_other*time_window_h+\\\\ tour_distance_other*log(1+distance)+\\\\ a700_a930_other*p_700a_930a+\\\\ a930_a1200_other*p_930a_1200a+\\\\ p300_p530_other*p_300p_530p+\\\\ p530_p730_other*p_530p_730p+\\\\ p730_p1000_other*p_730p_1000p+\\\\ p1000_a700_other*p_1000p_700a #V for quit V_quit=\",\n \"= Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for\",\n \"driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0) public_dummy_W=Beta('PT dummy in work',0,-10,10,0) work_tour_dummy_E=Beta('work\",\n \"dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy\",\n \"p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to 10pm in shopping',0,-10,10,0)\",\n \"import * from loglikelihood import * from statistics import * from nested import\",\n \"edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in shop',0,-10,10,1) other_logsum=Beta('other logsum in other',0,-10,10,1) time_window_work=Beta('time\",\n \"tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\\",\n \"work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0) student_dummy_W=Beta('student dummy in work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1)\",\n \"10pm to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period\",\n \"shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT dummy in shopping',0,-10,10,0) work_tour_dummy_O=Beta('work tour dummy in\",\n \"of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour',\",\n \"to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to\",\n \"shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1) first_tour_dummy_Q=Beta('first tour dummy\",\n \"for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy for second stop\",\n \"tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in\",\n \"in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for non-quit', 1.0,0,100,1)\",\n \"cons_work+\\\\ work_tour_dummy_W*1*(tour_type==1)+\\\\ edu_tour_dummy_W*1*(tour_type==2)+\\\\ shopping_tour_dummy_W*1*(tour_type==3)+\\\\ other_tour_dummy_W*1*(tour_type==4)+\\\\ female_dummy_W*female_dummy+\\\\ student_dummy_W*student_dummy+\\\\ worker_dummy_W*worker_dummy+\\\\ driver_dummy_W*driver_dummy+\\\\ passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum *\",\n \"dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1)\",\n \"p1000_a700_other*p_1000p_700a #V for quit V_quit= cons_Q+first_stop_inbound*first_stop*first_bound+\\\\ second_stop_inbound*second_stop*first_bound+\\\\ threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\\",\n \"= cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum\",\n \"edu',0,-10,10,0) passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in\",\n \"tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to\",\n \"in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in\",\n \"worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\ public_dummy_S*public_dummy+\\\\ shop_logsum * shoplogsum+\\\\ time_window_shopping*time_window_h+\\\\ tour_distance_shopping*log(1+distance)+\\\\ a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\\",\n \"tour dummy in work',0,-10,10,1) other_tour_dummy_W=Beta('other tour dummy in work',0,-10,10,1) female_dummy_W=Beta('female dummy in work',0,-10,10,0)\",\n \"from statistics import * from nested import * #import random cons_work= Beta('cons for\",\n \"a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\\",\n \"to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2 = Beta('MU for\",\n \"Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in\",\n \"passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a\",\n \"half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound=\",\n \"Beta('dummy for first stop of outbound half tour', 0,-10,10,0) second_stop_outbound= Beta('dummy for second\",\n \"shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0)\",\n \"stop of outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0)\",\n \"p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\ other_tour_dummy_O*1*(tour_type==4)+\\\\ female_dummy_O*female_dummy+\\\\ student_dummy_O*student_dummy+\\\\ worker_dummy_O*worker_dummy+\\\\\",\n \"dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student dummy in shopping',0,-10,10,1) worker_dummy_S=Beta('worker dummy\",\n \"dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in quit',0,-10,10,1)\",\n \"cons_other = Beta('cons for other',0,-10,10,0) cons_Q = Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for\",\n \"passenger_dummy_E=Beta('passenger dummy in edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1)\",\n \"work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in\",\n \"other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy in other',0,-10,10,0)\",\n \"to 5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm\",\n \"pm in other',0,-10,10,0) p730_p1000_other=Beta('period 7:30pm to 10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am\",\n \"quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour\",\n \"p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\\",\n \"tour dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in\",\n \"in edu',0,-10,10,0) tour_distance_shopping= Beta('log tour distance in shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0)\",\n \"edu',0,-10,10,0) public_dummy_E=Beta('PT dummy in edu',0,-10,10,0) work_tour_dummy_S=Beta('work tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy\",\n \"from nested import * #import random cons_work= Beta('cons for work', 0,-10,10,0) cons_edu =\",\n \"Beta('cons for quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1)\",\n \"quit',0,-10,10,1) first_stop_inbound= Beta('dummy for first stop of inbound half tour', 0,-10,10,1) second_stop_inbound= Beta('dummy\",\n \"tour',0,-10,10,0) threeplus_stop_inbound=Beta('dummy for 3+ stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first\",\n \"dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour\",\n \"Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance\",\n \"dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy\",\n \"in other',0,-10,10,0) work_logsum=Beta('work logsum in work',0,-10,10,1) edu_logsum=Beta('edu logsum in edu',0,-10,10,1) shop_logsum=Beta('shop logsum in\",\n \"other',0,-10,10,1) time_window_work=Beta('time available in work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available\",\n \"shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy\",\n \"in shopping',0,-10,10,1) time_window_other= Beta('time available in other',0,-10,10,1) tour_distance_work= Beta('log tour distance in work',0,-10,10,0)\",\n \"shopping',0,-10,10,0) tour_distance_other=Beta('log tour distance in other',0,-10,10,0) a700_a930_work= Beta('period 7am to 9:30am in work',0,-10,10,0)\",\n \"tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\ p730_p1000_edu*p_730p_1000p+\\\\ p1000_a700_edu*p_1000p_700a #V for shopping V_shopping = cons_shopping+\\\\\",\n \"dummy in shopping',0,-10,10,1) other_tour_dummy_S=Beta('other tour dummy in shopping',0,-10,10,0) female_dummy_S=Beta('female dummy in shopping',0,-10,10,0) student_dummy_S=Beta('student\",\n \"3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period\",\n \"prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10))\",\n \"to 7am in edu',0,-10,10,0) a700_a930_shopping= Beta('period 7am to 9:30am in shopping',0,-10,10,0) a930_a1200_shopping=Beta('period 9:30am\",\n \"p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\\",\n \"in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0) student_dummy_O=Beta('student dummy\",\n \"to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to\",\n \"from loglikelihood import * from statistics import * from nested import * #import\",\n \"in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm\",\n \"one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V = {0:V_quit,1: V_work,2:V_edu,3:V_shopping,4:V_other} av= {0:avail_quit,1:avail_workstop,2:avail_edustop,3:avail_shopstop,4:avail_otherstop} nest_quit = MU1 , [0] nest_nonquit\",\n \"rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter')\",\n \"work',0,-10,10,0) a930_a1200_work=Beta('period 9:30am to 12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0)\",\n \"other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\\",\n \"tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1) female_dummy_O=Beta('female dummy in other',0,-10,10,0)\",\n \"work_tour_dummy_O=Beta('work tour dummy in other',0,-10,10,0) edu_tour_dummy_O=Beta('edu tour dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy\",\n \"tour dummy in shopping',0,-10,10,1) edu_tour_dummy_S=Beta('edu tour dummy in shopping',0,-10,10,1) shopping_tour_dummy_S=Beta('shopping tour dummy in\",\n \"12pm in work',0,-10,10,0) p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30\",\n \"for education V_edu = cons_edu+\\\\ work_tour_dummy_E*1*(tour_type==1)+\\\\ edu_tour_dummy_E*1*(tour_type==2)+\\\\ shopping_tour_dummy_E*1*(tour_type==3)+\\\\ other_tour_dummy_E*1*(tour_type==4)+\\\\ female_dummy_E*female_dummy+\\\\ student_dummy_E*student_dummy+\\\\ worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\\",\n \"12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0) p530_p730_edu=Beta('period 5:30pm to 7:30\",\n \"Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period\",\n \"to 7:30 pm in edu',0,-10,10,0) p730_p1000_edu=Beta('period 7:30pm to 10pm in edu',0,-10,10,0) p1000_a700_edu=Beta('period 10pm\",\n \"exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude nullLoglikelihood(av,'obsIter') choiceSet = [0,1,2,3,4] cteLoglikelihood(choiceSet,stop_type,'obsIter')\",\n \"stop of inbound half tour',0,-10,10,0) first_stop_outbound= Beta('dummy for first stop of outbound half\",\n \"a700_a930_shopping*p_700a_930a+\\\\ a930_a1200_shopping*p_930a_1200a+\\\\ p300_p530_shopping*p_300p_530p+\\\\ p530_p730_shopping*p_530p_730p+\\\\ p730_p1000_shopping*p_730p_1000p+\\\\ p1000_a700_shopping*p_1000p_700a #V for other V_other=cons_other+\\\\ work_tour_dummy_O*1*(tour_type==1)+\\\\ edu_tour_dummy_O*1*(tour_type==2)+\\\\ shopping_tour_dummy_O*1*(tour_type==3)+\\\\\",\n \"work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in\",\n \"dummy in other',0,-10,10,0) shopping_tour_dummy_O=Beta('shopping tour dummy in other',0,-10,10,0) other_tour_dummy_O=Beta('other tour dummy in other',0,-10,10,1)\",\n \"p300_p530_work=Beta('period 3pm to 5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0)\",\n \"threeplus_stop_inbound*three_plus_stop*first_bound+\\\\ first_stop_outbound*first_stop*second_bound+\\\\ second_stop_outbound*second_stop*second_bound+\\\\ threeplus_stop_outbound*three_plus_stop*second_bound+\\\\ work_tour_dummy_Q*1*(tour_type==1)+\\\\ edu_tour_dummy_Q*1*(tour_type==2)+\\\\ shopping_tour_dummy_Q*1*(tour_type==3)+\\\\ other_tour_dummy_Q*1*(tour_type==4)+\\\\ first_tour_dummy_Q*first_tour_dummy+\\\\ sub_tour_dummy_Q*has_subtour+zero_tour_remain_Q*1*(tour_remain==0)+\\\\ one_tour_remain_Q*1*(tour_remain==1)+twoplus_tour_remain_Q*1*(tour_remain>=2) V =\",\n \"edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am to 12pm in edu',0,-10,10,0) p300_p530_edu=Beta('period 3pm to 5:30pm in edu',0,-10,10,0)\",\n \"5:30pm in work',0,-10,10,0) p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to\",\n \"worker_dummy_S=Beta('worker dummy in shopping',0,-10,10,0) driver_dummy_S=Beta('driver dummy in shopping',0,-10,10,0) passenger_dummy_S=Beta('passenger dummy in shopping',0,-10,10,0) public_dummy_S=Beta('PT\",\n \"cons_work= Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons\",\n \"= Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for\",\n \"dummy in quit',0,-10,10,1) edu_tour_dummy_Q=Beta('edu tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1)\",\n \"9:30am to 12pm in other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm\",\n \"zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+ tour remain dummy',0,-10,10,1) work_tour_dummy_W=Beta('work\",\n \"Beta('cons for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for\",\n \"Beta('log tour distance in work',0,-10,10,0) tour_distance_edu= Beta('log tour distance in edu',0,-10,10,0) tour_distance_shopping= Beta('log\",\n \"a700_a930_other= Beta('period 7am to 9:30am in other',0,-10,10,0) a930_a1200_other=Beta('period 9:30am to 12pm in other',0,-10,10,0)\",\n \"in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1) driver_dummy_E=Beta('driver dummy in\",\n \"for work', 0,-10,10,0) cons_edu = Beta('cons for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0)\",\n \"10pm in other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for\",\n \"work',0,-10,10,1) time_window_edu= Beta('time available in edu',0,-10,10,1) time_window_shopping= Beta('time available in shopping',0,-10,10,1) time_window_other= Beta('time\",\n \"tour dummy in quit',0,-10,10,1) shopping_tour_dummy_Q=Beta('shopping tour dummy in quit',0,-10,10,1) other_tour_dummy_Q=Beta('other tour dummy in\",\n \"work',0,-10,10,1) worker_dummy_W=Beta('worker dummy in work',0,-10,10,1) driver_dummy_W=Beta('driver dummy in work',0,-10,10,0) passenger_dummy_W=Beta('passenger dummy in work',0,-10,10,0)\",\n \", [1,2,3,4] nests=nest_quit,nest_nonquit prob = nested(V,av,nests,stop_type) #prob = bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter')\",\n \"outbound half tour',0,-10,10,0) threeplus_stop_outbound=Beta('dummy for 3+ stop of outbound half tour',0,-10,10,0) work_tour_dummy_Q=Beta('work tour\",\n \"subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain dummy',0,-10,10,0) twoplus_tour_remain_Q=Beta('2+\",\n \"5:30pm in shopping',0,-10,10,0) p530_p730_shopping=Beta('period 5:30pm to 7:30 pm in shopping',0,-10,10,0) p730_p1000_shopping=Beta('period 7:30pm to\",\n \"worker_dummy_E*worker_dummy+\\\\ driver_dummy_E*driver_dummy+\\\\ passenger_dummy_E*passenger_dummy+\\\\ public_dummy_E*public_dummy+\\\\ edu_logsum * edulogsum+\\\\ time_window_edu*time_window_h+\\\\ tour_distance_edu*log(1+distance)+\\\\ a700_a930_edu*p_700a_930a+\\\\ a930_a1200_edu*p_930a_1200a+\\\\ p300_p530_edu*p_300p_530p+\\\\ p530_p730_edu*p_530p_730p+\\\\\",\n \"pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0) p1000_a700_work=Beta('period 10pm to 7am\",\n \"worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a #V for education V_edu\",\n \"work_tour_dummy_W=Beta('work tour dummy in work',0,-10,10,1) edu_tour_dummy_W=Beta('edu tour dummy in work',0,-10,10,1) shopping_tour_dummy_W=Beta('shopping tour dummy\",\n \"shopping V_shopping = cons_shopping+\\\\ work_tour_dummy_S*1*(tour_type==1)+\\\\ edu_tour_dummy_S*1*(tour_type==2)+\\\\ shopping_tour_dummy_S*1*(tour_type==3)+\\\\ other_tour_dummy_S*1*(tour_type==4)+\\\\ female_dummy_S*female_dummy+\\\\ student_dummy_S*student_dummy+\\\\ worker_dummy_S*worker_dummy+\\\\ driver_dummy_S*driver_dummy+\\\\ passenger_dummy_S*passenger_dummy+\\\\\",\n \"to 7am in work',0,-10,10,0) a700_a930_edu= Beta('period 7am to 9:30am in edu',0,-10,10,0) a930_a1200_edu=Beta('period 9:30am\",\n \"passenger_dummy_W*passenger_dummy+\\\\ public_dummy_W*public_dummy+\\\\ work_logsum * worklogsum+\\\\ time_window_work*time_window_h+\\\\ tour_distance_work*log(1+distance)+\\\\ a700_a930_work*p_700a_930a+\\\\ a930_a1200_work*p_930a_1200a+\\\\ p300_p530_work*p_300p_530p+\\\\ p530_p730_work*p_530p_730p+\\\\ p730_p1000_work*p_730p_1000p+\\\\ p1000_a700_work*p_1000p_700a\",\n \"quit',0,-10,10,0) sub_tour_dummy_Q=Beta('has subtour dummy in quit',0,-10,10,0) zero_tour_remain_Q=Beta('zero tour remain dummy',0,-10,10,1) one_tour_remain_Q=Beta('one tour remain\",\n \"for education',0,-50,10,0) cons_shopping = Beta('cons for shopping',0,-10,10,0) cons_other = Beta('cons for other',0,-10,10,0) cons_Q\",\n \"other',0,-10,10,0) p1000_a700_other=Beta('period 10pm to 7am in other',0,-10,10,0) MU1 = Beta('MU for quit',1,0,100,1) MU2\",\n \"edu',0,-10,10,1) female_dummy_E=Beta('female dummy in edu',0,-10,10,0) student_dummy_E=Beta('student dummy in edu',0,-10,10,1) worker_dummy_E=Beta('worker dummy in edu',0,-10,10,1)\",\n \"other',0,-10,10,0) p300_p530_other=Beta('period 3pm to 5:30pm in other',0,-10,10,0) p530_p730_other=Beta('period 5:30pm to 7:30 pm in\",\n \"bioLogit(V,av,stop_type) rowIterator('obsIter') BIOGEME_OBJECT.ESTIMATE = Sum(log(prob),'obsIter') exclude = ((avail_violation==1)+(origin_mtz==0)+(destination_mtz==0)+(time_window_h>=10)) > 0 BIOGEME_OBJECT.EXCLUDE = exclude\"\n]","isConversational":false}}},{"rowIdx":3467,"cells":{"ngram":{"kind":"list like","value":["from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app():","# 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) #","from app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from","# 初始化模块 from config import Config from flask import Flask from flask_sqlalchemy import","# 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee #","数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__)","post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary","职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统","app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view","通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统","salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary)","flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() #","from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy #","flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from","= SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置","# 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department #","db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post","from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy()","import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import","from app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from","department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance","from app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee)","初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import","import post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import","import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app","SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app","import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db","employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department","config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象","from app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统 from","app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view","db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) #","创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库","import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department)","Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db =","flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): #","# 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app =","工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本","import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import","初始化模块 from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy","# 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary #","员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统","app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post)","部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统","# 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance #","app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from","# 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance)","考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance) return","# 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post #","from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance) return app","Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def","import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象","# 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view","# flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统","Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee","app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view","SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config)","def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app)","create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) #","= Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import","app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view"],"string":"[\n \"from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app():\",\n \"# 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) #\",\n \"from app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from\",\n \"# 初始化模块 from config import Config from flask import Flask from flask_sqlalchemy import\",\n \"# 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee #\",\n \"数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__)\",\n \"post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary\",\n \"职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统\",\n \"app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view\",\n \"通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统\",\n \"salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary)\",\n \"flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() #\",\n \"from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy #\",\n \"flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from\",\n \"= SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置\",\n \"# 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department #\",\n \"db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post\",\n \"from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy()\",\n \"import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import\",\n \"from app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from\",\n \"department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance\",\n \"from app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee)\",\n \"初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import\",\n \"import post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import\",\n \"import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app\",\n \"SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app\",\n \"import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db\",\n \"employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import department\",\n \"config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象\",\n \"from app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统 from\",\n \"app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view\",\n \"db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) #\",\n \"创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库\",\n \"import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department)\",\n \"Config from flask import Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db =\",\n \"flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): #\",\n \"# 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象 app =\",\n \"工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本\",\n \"import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from app.view import\",\n \"初始化模块 from config import Config from flask import Flask from flask_sqlalchemy import SQLAlchemy\",\n \"# 部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary #\",\n \"员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统\",\n \"app.view import salary # 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post)\",\n \"部门管理子系统 from app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统\",\n \"# 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view import attendance #\",\n \"app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from\",\n \"# 考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance)\",\n \"考勤管理子系统 from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance) return\",\n \"# 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view import post #\",\n \"from app.view import attendance # 统一对外接口蓝本 app.register_blueprint(employee) app.register_blueprint(post) app.register_blueprint(department) app.register_blueprint(salary) app.register_blueprint(attendance) return app\",\n \"Flask from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def\",\n \"import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app(): # flask操作对象\",\n \"# 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee # 职位管理子系统 from app.view\",\n \"# flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统\",\n \"Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import employee\",\n \"app.view import department # 工资管理子系统 from app.view import salary # 考勤管理子系统 from app.view\",\n \"SQLAlchemy() # 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config)\",\n \"def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app)\",\n \"create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) #\",\n \"= Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) # 初始化数据库 db.init_app(app) # 员工管理子系统 from app.view import\",\n \"app.view import post # 部门管理子系统 from app.view import department # 工资管理子系统 from app.view\"\n]","isConversational":false}}},{"rowIdx":3468,"cells":{"ngram":{"kind":"list like","value":[") def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def","__open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def contract(self): return","def contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self): return","url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property","self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path):","def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path =","url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self):","eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract(","def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def contract(self):","def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property","__init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def","self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path,","def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self,","contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth","= web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self):","class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 =","return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address","self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property","abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return","= self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi","url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn","__init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path","@property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self):","self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address @property def","self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract","self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node","self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi )","self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def","import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node)","@property def contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self):","return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address,","import json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider","web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return","return self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address @property","address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self): return self._eth_conn.w3","ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path","= abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self):","self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return","@property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn","json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property","abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property","self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address,","web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection():","json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider =","class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address)","@property def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def","eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi","web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3","abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address","self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def","self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def","return self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property","= self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self):","self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return","abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi()","\"r\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def","def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self): return","= self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\"))","= web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node class","<filename>listener/src/ethereum_connection.py<gh_stars>0 import json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node","EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider)","w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn,","contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi =","self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def","= url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3","address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi","@property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self):","def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self): return","return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract","= eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract =","self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address =","def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn ="],"string":"[\n \") def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property def\",\n \"__open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property def contract(self): return\",\n \"def contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self): return\",\n \"url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property\",\n \"self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path):\",\n \"def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path =\",\n \"url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self):\",\n \"eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract(\",\n \"def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property def contract(self):\",\n \"def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property\",\n \"__init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def\",\n \"self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path,\",\n \"def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self,\",\n \"contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth\",\n \"= web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self):\",\n \"class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 =\",\n \"return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address\",\n \"self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property\",\n \"abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return\",\n \"= self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi\",\n \"url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn\",\n \"__init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path\",\n \"@property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self):\",\n \"self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address @property def\",\n \"self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract\",\n \"self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node\",\n \"self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi )\",\n \"self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def\",\n \"import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node)\",\n \"@property def contract(self): return self._contract @property def address(self): return self._contract_address @property def eth(self):\",\n \"return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address,\",\n \"import json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider\",\n \"web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return\",\n \"return self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address @property\",\n \"address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self): return self._eth_conn.w3\",\n \"ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path\",\n \"= abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self):\",\n \"self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return\",\n \"@property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn\",\n \"json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property\",\n \"abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property\",\n \"self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address,\",\n \"web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection():\",\n \"json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider =\",\n \"class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address)\",\n \"@property def w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def\",\n \"eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi\",\n \"web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3\",\n \"abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self): return self._contract_address\",\n \"self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def\",\n \"self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def\",\n \"return self._contract @property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property\",\n \"= self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self):\",\n \"self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return\",\n \"abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi()\",\n \"\\\"r\\\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def\",\n \"def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self): return\",\n \"= self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\"))\",\n \"= web3.Web3(self._node_provider) @property def w3(self): return self._w3 @property def url_node(self): return self._url_node class\",\n \"<filename>listener/src/ethereum_connection.py<gh_stars>0 import json import web3 class EthereumConnection(): def __init__(self, url_node): self._url_node = url_node\",\n \"EthereumConnection(): def __init__(self, url_node): self._url_node = url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider)\",\n \"w3(self): return self._w3 @property def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn,\",\n \"contract_address, abi_path): self._eth_conn = eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi =\",\n \"self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract = self._eth_conn.w3.eth.contract( address=self._contract_address, abi=self.__json_abi ) def\",\n \"= url_node self._node_provider = web3.HTTPProvider(self._url_node) self._w3 = web3.Web3(self._node_provider) @property def w3(self): return self._w3\",\n \"address=self._contract_address, abi=self.__json_abi ) def __open_abi(self): return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi\",\n \"@property def address(self): return self._contract_address @property def eth(self): return self._contract.web3.eth @property def w3(self):\",\n \"def abi(self): return self.__json_abi @property def contract(self): return self._contract @property def address(self): return\",\n \"return json.load(open(self._abi_path, \\\"r\\\")) @property def abi(self): return self.__json_abi @property def contract(self): return self._contract\",\n \"= eth_conn self._contract_address = self._eth_conn.w3.toChecksumAddress(contract_address) self._abi_path = abi_path self.__json_abi = self.__open_abi() self._contract =\",\n \"self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn = eth_conn self._contract_address =\",\n \"def url_node(self): return self._url_node class ContractConnection(): def __init__(self, eth_conn, contract_address, abi_path): self._eth_conn =\"\n]","isConversational":false}}},{"rowIdx":3469,"cells":{"ngram":{"kind":"list like","value":["fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in","is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile =","p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50","np.pi if i < 0 else i for i in aux_phase] ) angle","axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\"","rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number of","row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\",","the rotor system in a 2D view. Parameters ---------- node : int Select","= np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +","plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method plots the","kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]):","results and provide plots for Time Response and Orbit Response. Parameters ---------- time_range","k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude,","{i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude:","in rad/s. wd : array Array with the damped natural frequencies log_dec :","The figure object with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval)","rad/s. wd : array Array with the damped natural frequencies log_dec : array","legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i, p","name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=(","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict(","each frequency. phase : array Phase of the frequency response for node for","natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles","between 0% and 100% inclusive. units : str, optional Unit system Default is","None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]:","optional Additional plot axes kwargs : optional Additional key word arguments can be","number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency","p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1}","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\",","(dB) of the frequency response for each pair input/output. phase : array Array","k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure()","kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3,","go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n +","graph_objects.Figure() The figure object with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval =","= wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the","+ p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2,","elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude","+ 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, )","r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig =","enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i","axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe","= 0 color_p = 0 for i, p in enumerate(probe): dofx = p[0]","- p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50","compute, which must be between 0 and 100 inclusive. args: optional harmonics :","in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval,","/ 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace(","in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 =","r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\"","), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\",","plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs frequency. Parameters","colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide","col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1)","np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0 color_i =","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[],","int indicate the node where the probe is located. orientation : float, probe","k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 =",") fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), )","2}], [{}, None]] ) # fmt: on for data in fig0[\"data\"]: data.showlegend =","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[],","plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response. This","This method plots the time response given a tuple of probes with their","The figure object with the plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\")","for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1)","\"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) )","return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This","Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional Additional key","aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range,","hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i, p in","probe is located. orientation : float, probe orientation angle about the shaft. The","fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}],","col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1,","1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval:","interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, )","also excite a specific natural frequency. Two options for plooting are available: Matplotlib","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[],","i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1)","in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof","{}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs,","from plotly import graph_objects as go from plotly import io as pio from","1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\"","1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x","for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5,","The figure object with the plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof,","figure object with the plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval","= px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide plots for Campbell Diagram.","%{y:.2e}\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp,","with the speed range in rad/s. wd : array Array with the damped","angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class","and an input. Parameters ---------- percentile : list, optional Sequence of percentiles to","interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\"","go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j,","with the plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif","2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal","go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval:","* n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\",","Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes","response using Plotly. Parameters ---------- probe : list of tuples List with tuples","np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] *","fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0),","graph_objects.Figure() The figure object with the plot. \"\"\" if units == \"m\": y_axis_label","available: Matplotlib and Bokeh. The user chooses between them using the attribute plot_type.","fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional","mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p /","axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for","with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with","col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data,","Bokeh. The user chooses between them using the attribute plot_type. The default is","\"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units ==","%{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[...,","nodes from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof :","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\",","\"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v","50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p /","of analyses in st_rotor_assembly.py. \"\"\" import numpy as np from plotly import express","Degree of freedom. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs","operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2","legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, )","forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof","1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x","for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2,","axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs,","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p),","line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j +","degrees of freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure()","freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(",") # fmt: on for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1,","fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend =","\"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j],","n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0","Number of degrees of freedom per shaft element's node Returns ------- fig :","mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i, p","for node for each frequency. number_dof = int Number of degrees of freedom","set of probes. Parameters ---------- dof : int Degree of freedom to observe","50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p /","** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe","legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", ) ) color_i += 1","line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase:","provide plots for Forced Response. Parameters ---------- force_resp : array Array with the","col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), )","%{y:.2f}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p,","Python Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly","showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile:","class ST_CampbellResults: \"\"\"Store stochastic results and provide plots for Campbell Diagram. It's possible","False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2,","name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for","i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]),",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],","\"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase","name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile:","fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i +","1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval:","xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), )","fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This function plots orbits for each","%{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list): x","amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase,",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6,","fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 =","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p,","- Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), )","= \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in","line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time:","in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2,","+ 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\",","x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50","1x. kwargs : optional Additional key word arguments can be passed to change","axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3,","fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" +","- Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase:","x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n","axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1}","Array with the frequencies. magnitude : array Magnitude of the frequency response for","percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This method plots the","= np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @","- p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\",","frequency_range : array Array with the frequencies. magnitude : array Magnitude of the","+ np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude):","node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This function plots","information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot.","subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900,","borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ):","2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence","fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y -","p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50","speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\",","enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase,","go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude:","This method plots the frequency response magnitude given an output and an input","enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof *","color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X","figure with diagrams for frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval,","response. This method plots the time response given a tuple of probes with","fig is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile","fig=None, *args, **kwargs ): \"\"\"Plot time response. This method plots the time response","== 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\"","j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5,","height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), )","Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d(","row=1, col=1) for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for","/ 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5,","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" +","aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp,","fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3,","node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\"","legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p in","int Degree of freedom to observe the response. percentile : list, optional Sequence","np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag","axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\"","np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:,","\"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1 for j,","on for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data",") ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, )","plots for Forced Response. Parameters ---------- force_resp : array Array with the force","\"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile):","x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency:","for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1)","which must be between 0 and 100 inclusive. args: optional harmonics : list,","p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i),","r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None: fig = go.Figure() color_p =","1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node","= dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label","with amplitude vs frequency phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval,","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and","= nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[],","Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot","* self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i <","Plotly Python Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots()","Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor","opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=(","+ \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1 for","Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \"\"\" def","+X direction. percentile : list, optional Sequence of percentiles to compute, which must","fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof *","dof : int Degree of freedom to observe the response. percentile : list,","{}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\",","color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j + 1),","line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for i,","= make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for","Phase of the frequency response for node for each frequency. number_dof = int","legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) )","hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return","between 0 and 100 inclusive. kwargs : optional Additional key word arguments can","cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt: on for","subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec.","axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x,","the frequency response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots()","- Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and",") angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0),","\"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar(","dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs)","for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with","response. This method plots the phase response given a set of probes. Parameters","compute, which must be between 0 and 100 inclusive. conf_interval : list, optional","opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"),","response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure","hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i, p","+ 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in","speed_range : array Array with the speed range in rad/s. magnitude : array","polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return","dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args,",") fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args,","between 0 and 100 inclusive. harmonics: list, optional List withe the harmonics to","), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for","percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the","%{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","= np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3,","fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in","1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in enumerate(percentile):","+ 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1","fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout(","subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis,","mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i +","type of analyses in st_rotor_assembly.py. \"\"\" import numpy as np from plotly import","and log dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd","), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0),","phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if","{p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", )","name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency:","+ 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3,","line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ),","x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\"","2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:,","kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in","- Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace(","probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) **","Default is \"mic-pk-pk\" polar_kwargs : optional Additional key word arguments can be passed","fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs):","line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) )","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\",","np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 +","Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(","Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) )","), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for","must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14),","r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v","units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots the frequency and phase response","given a set of probes. Parameters ---------- probe : list of tuples List","* n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j ==","using the attribute plot_type. The default is bokeh Parameters ---------- speed_range : array","class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for Time Response and Orbit","**kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units,","plots the frequency response magnitude given an output and an input using Plotly.","go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude:","{\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1,","width=1000, height=800, ...). *See Plotly Python Figure Reference for more information. Returns -------","), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0","per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with","name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency:","# fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe","customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" +","percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ----------","p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0)",") fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout(","This module returns graphs for each type of analyses in st_rotor_assembly.py. \"\"\" import","each node on the rotor system in a 3D view. Parameters ---------- percentile","Additional key word arguments can be passed to change the plot layout only","height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def","* node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6,","data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis,","else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None: fig = go.Figure() color_p","+ 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\"","figure object with the plot. \"\"\" if fig is None: fig = go.Figure()","log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to","fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" +","time response. This method plots the time response given a tuple of probes","in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2,","= go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,","phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[],","enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] *","speed range in rad/s. wd : array Array with the damped natural frequencies","word arguments can be passed to change the plot layout (e.g. width=800, height=600,","np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\",","must be between 0 and 100 inclusive. args: optional harmonics : list, optional","p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude:","= 0 color_i = 0 for i, p in enumerate(probe): dofx = p[0]","Array with the damped natural frequencies log_dec : array Array with the Logarithmic","0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy =",":, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx],","- Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *","+ (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :,","object with the plot. units : str, optional Unit system Default is \"mic-pk-pk\".","- percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" +","p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","frequency and phase response given a set of probes. Parameters ---------- dof :","vs frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs)","p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"),","conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile","fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\",","object with the plot. args : optional Additional plot axes kwargs : optional","n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n +","+ p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2,","- p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1,","** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace(","interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", ) ) color_i","= False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data,","y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, )","vs frequency phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs)","respective orbit response. percentile : list, optional Sequence of percentiles to compute, which","Plotly graph_objects.Figure() The figure object with the plot. units : str, optional Unit","opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i","2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter(","y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile:","50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1,","v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots","angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None,","line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1} -","p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 +","= yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof =","%{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[...,","def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ----------","p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])),","(μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval)","default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\",","self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural","input using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to","r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1}","node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"),","from plotly import express as px from plotly import graph_objects as go from","np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p","axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(","**kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]]","linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2,","axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence","int Number of degrees of freedom per shaft element's node. Returns ------- subplots","= np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 -","p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5,","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self,","speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural","name=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"),","opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) )","return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced","each frequency. frequency_range : array Array with the frequencies. magnitude : array Magnitude","1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot(","self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot","y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency:","j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j","hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1]","y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p),","response for node for each frequency. number_dof = int Number of degrees of","figure object with the plot. args : optional Additional plot axes kwargs :","frequencies, phase of the frequency response for each pair input/output. Returns ------- subplots","Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure","off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp =","fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\"","Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide","confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase","frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1","k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1),","kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy","line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"),","frequency. This method plots the frequency response magnitude given an output and an","user chooses between them using the attribute plot_type. The default is bokeh Parameters","go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\"","x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"),","+ p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2,","ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X -","in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0,","\"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(","default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile,","units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label =","fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots","**kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\"","subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis,","hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for","Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence","ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for Time Response and Orbit Response.","%{customdata:.2f}</b>\" ), ) ) color_p += 1 for j, p in enumerate(conf_interval): p1","rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt: on","List withe the harmonics to be plotted. The default is to plot 1x.","frequency and phase response given an output and an input. Parameters ---------- percentile","1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase,","0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the","speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) )","+= 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p","50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1],","fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots =","showlegend=False, mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) *","the plot. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs :","line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude:","units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the unbalance response magnitude.","interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\"","plots the phase response given an output and an input using bokeh. Parameters","\"mic-pk-pk\" polar_kwargs : optional Additional key word arguments can be passed to change","{}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal","**kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self,","go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} -","more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude","color_i = 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof","---------- dof : int Degree of freedom to observe the response. percentile :","n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if","\"\"\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list,","tableau_colors pio.renderers.default = \"browser\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24","[np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in","must be between 0 and 100 inclusive. kwargs : optional Additional key word","kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs)",") for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p /","[[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for","orbit response (3D). This function plots orbits for each node on the rotor","Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"),","The figure object with the plot. units : str Magnitude unit system. Default","plotting module. This module returns graphs for each type of analyses in st_rotor_assembly.py.","intervals to compute, which must be between 0% and 100% inclusive. fig :","np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) )","i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]),","50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p /","response. This method plots the unbalance response magnitude. Parameters ---------- probe : list","= speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[],","axial positions. number_dof : int Number of degrees of freedom per shaft element's","Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with","magnitude (dB) of the frequency response for each pair input/output. phase : array","pio.renderers.default = \"browser\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2","def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" def __init__(self, time_range, yout,","2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2,","%{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i,","enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0)","kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3,","percentile = np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units",") ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p /","go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False,","{}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" +","if fig is None: fig = go.Figure() color_p = 0 color_i = 0",": str, optional Unit system Default is \"mic-pk-pk\". kwargs : optional Additional key","- p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1],","title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\",","%{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j],","Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array.","mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ),","j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i]",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" ndof = self.number_dof","**kwargs): \"\"\"Plot frequency response. This method plots the phase response given a set","angle response. This method plots the phase response given an output and an","j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array(","import tableau_colors pio.renderers.default = \"browser\" # set Plotly palette of colors colors1 =","(dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar(",") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0),","{i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p +=","fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1)","default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range,","plot axes with magnitude plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude","= phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs","optional List withe the harmonics to be plotted. The default is to plot","Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional key word arguments","%{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):","p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50","number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range","units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- dof :","p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]]","), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response.","* np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on","percentile : list, optional Sequence of percentiles to compute, which must be between","1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)],","of the frequency response for each pair input/output. phase : array Array with","units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional","= p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0,","the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\":","name=f\"Probe {i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} -","\"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout","i), hoverinfo=\"none\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j],","fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters","\"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in","node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5,","theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\",","{}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) )","log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[],","p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","optional Unit system Default is \"mic-pk-pk\". kwargs : optional Additional key word arguments","1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j ==","in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = (","def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This function","color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j","100 inclusive. units : str, optional Unit system Default is \"mic-pk-pk\" kwargs :","fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter(",": int Degree of freedom. units : str Magnitude unit system. Default is","# fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1}","{p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return","scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ),","list, optional List with the harmonics to be plotted. The default is to","fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3,","---------- time_range : 1-dimensional array Time array. yout : array System response. xout","theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} -","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\",","diagrams for frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs)","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p","* node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X","the frequency response for node for each frequency. number_dof = int Number of","= np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0 color_p","(e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information.","fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe","color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[],","y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X","%{y:.3f}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j],","width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class","name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x =","with magnitude plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif","fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i +","name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i, p in enumerate(conf_interval):",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1),","Sequence of confidence intervals to compute, which must be between 0 and 100","\"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile =","Plotly. Parameters ---------- probe : list of tuples List with tuples (node, orientation","go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i),","the plot. args : optional Additional plot axes kwargs : optional Additional key",") ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[],","**kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p","str, optional Unit system Default is \"mic-pk-pk\". kwargs : optional Additional key word","axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0)","1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) )","{i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p","%{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter(","import numpy as np from plotly import express as px from plotly import","Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p","the force response for each node for each frequency. frequency_range : array Array","an output and an input using bokeh. Parameters ---------- percentile : list, optional","y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\"","title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\",","display the respective orbit response. percentile : list, optional Sequence of percentiles to",": array Array with the speed range in rad/s. wd : array Array","line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout(","phase angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range","can be passed to change the plot layout only (e.g. width=1000, height=800, ...).","= np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 +","optional Unit system Default is \"mic-pk-pk\" kwargs : optional Additional key word arguments","z=np.percentile( self.yout[..., ndof * n + 1], 50 - p / 2, axis=0","), ) return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response.","p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2","enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp,",") fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[],","phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i","v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics):","frequency response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly","fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot","fig is None: fig = go.Figure() color_i = 0 color_p = 0 for","p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\"","+ 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof *","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\",","x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50",") ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None,","- Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec:","\"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p in enumerate(percentile):","magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot","= np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 -",": list of tuples List with tuples (node, orientation angle). node : int","+ 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\"","Additional key word arguments can be passed to change the plot layout (e.g.","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p in enumerate(percentile):","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store","customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase:","= \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else:","= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None:","frequency response for node for each frequency. phase : array Phase of the",") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase,","Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout : array","fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency","hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude:","1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /","inclusive. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs : optional","fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args,","go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v","figure object with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile","thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\"","self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[],","for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2,","\"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) )","linecolor=\"black\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs):","dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in","positions. number_dof : int Number of degrees of freedom per shaft element's node","with tuples (node, orientation angle). node : int indicate the node where the","input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency","0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the","plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response","model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number of degrees","view. Parameters ---------- node : int Select the node to display the respective","plotly import express as px from plotly import graph_objects as go from plotly","for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 +","1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude:",") for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p,","stochastic results and provide plots for Time Response and Orbit Response. Parameters ----------","= np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 -","- p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p),","passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python","+ p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2,","thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) )","/ 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if","rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for data in","= number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot","vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude =","title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), )","of confidence intervals to compute, which must be between 0 and 100 inclusive.",") fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2,","line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1} -","line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ),","with diagrams for frequency and log dec. \"\"\" def __init__(self, speed_range, wd, log_dec):","are available: Matplotlib and Bokeh. The user chooses between them using the attribute",") ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 =","\"polar\", \"rowspan\": 2}], [{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1)","for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0]","to change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure","args : optional Additional plot axes kwargs : optional Additional key word arguments","\"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =","\"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self,","is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line,","each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude","%{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def","y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase:","with the frequencies, magnitude (dB) of the frequency response for each pair input/output.","2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i],","%{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","Magnitude of the frequency response for node for each frequency. phase : array","System response. xout : array Time evolution of the state vector. nodes_list: array","enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j],","j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[...,","probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly.","# fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe,","range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1),","{p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) )","2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter(","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[],","object with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile =","percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p","magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase","- Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) )","+ 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\",","go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile(","**kwargs, ): \"\"\"Plot frequency response. This method plots the unbalance response magnitude. Parameters","fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis,","/ 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace(","Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],","x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0,","optional harmonics : list, optional List with the harmonics to be plotted. The","the speed range in rad/s. wd : array Array with the damped natural","for Frequency Response. Parameters ---------- speed_range : array Array with the speed range","---------- node : int Select the node to display the respective orbit response.","with the plot. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs","enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j],","y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof","data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend = False","color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p","plot. \"\"\" if fig is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval","plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell Diagram.","{i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence","= magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude(","percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", )","(dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v",": optional Additional plot axes kwargs : optional Additional key word arguments can","self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe,","only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more information.",":, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx],",") ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe,","self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def","%{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x,",") return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs","subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis,","name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, )","forced response using Plotly. Parameters ---------- probe : list of tuples List with","p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1)","an input using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles","\"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,","[{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in","and orientations. Parameters ---------- probe : list of tuples List with tuples (node,",") ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *","p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50","self.yout[..., ndof * n + 1], 50 + p / 2, axis=0 ),","fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes(","in a 3D view. Parameters ---------- percentile : list, optional Sequence of percentiles","on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\",","in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter(","refers to +X direction. percentile : list, optional Sequence of percentiles to compute,","\"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v","unit system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional key word arguments can","object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units","%{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig","), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,","plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots the frequency","self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This method plots","state vector. nodes_list: array list with nodes from a rotor model. nodes_pos: array","of freedom to observe the response. percentile : list, optional Sequence of percentiles","in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p,","harmonics to be plotted. The default is to plot 1x. kwargs : optional","z=np.percentile( self.yout[..., ndof * n + 1], 50 + p / 2, axis=0","dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1])","fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label,","- confidence interval: {p}%\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"),","0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:,","= operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle)","/ 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 +","p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence","50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node +","= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace(","- Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for","Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs,","the phase response given a set of probes. Parameters ---------- probe : list",": int Select the node to display the respective orbit response. percentile :","x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=(","** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3,","force_resp : array Array with the force response for each node for each","as go from plotly import io as pio from plotly.subplots import make_subplots from","fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j","= np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2, axis=0)","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","located. orientation : float, probe orientation angle about the shaft. The 0 refers","harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile :","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\",","object with the plot. kwargs : optional Additional key word arguments can be","fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase","each type of analyses in st_rotor_assembly.py. \"\"\" import numpy as np from plotly","%{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude,","array Array with the force response for each node for each frequency. frequency_range","\"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt: on for data in fig0[\"data\"]:","%{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p","j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude:","figure object with the plot. kwargs : optional Additional key word arguments can","_probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) #","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20),","p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50","must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure","frequencies, magnitude (dB) of the frequency response for each pair input/output. phase :","fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- probe","self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude","operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:,","0 color_i = 0 for i, p in enumerate(probe): dofx = p[0] *","for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range,","array System response. xout : array Time evolution of the state vector. nodes_list:","= probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]),","and provide plots for Time Response and Orbit Response. Parameters ---------- time_range :","Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list, optional","None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0,","node for each frequency. frequency_range : array Array with the frequencies. magnitude :","np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p","* self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1] #",") for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50","percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method plots the phase response","**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 +","in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0]","express as px from plotly import graph_objects as go from plotly import io","np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,","plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to","frequency phase angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range =",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if units ==","line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" +","enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude,","response. percentile : list, optional Sequence of percentiles to compute, which must be",": Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \"\"\"","make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set Plotly palette of","50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\",",") fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), )","row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis,","nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list =","must be between 0% and 100% inclusive. units : str, optional Unit system",") return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for Time","p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" +","axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else","= np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 +","change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference","shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object with","axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase,","p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2","1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1","subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in","color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\"","x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" +","\"\"\"Store stochastic results and provide plots for Time Response and Orbit Response. Parameters","response for each node for each frequency. frequency_range : array Array with the","of confidence intervals to compute, which must be between 0% and 100% inclusive.","interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\",","for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode","figure object with the plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list,","+ \"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(","confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label)","and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals to compute,","conf_interval : list, optional Sequence of confidence intervals to compute, which must be","2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence","magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude(","axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,","for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1),","frequency phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values","array Array with the frequencies, phase of the frequency response for each pair","np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p","Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot","fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval:","to compute, which must be between 0 and 100 inclusive. units : str,","for frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values","and provide plots for Forced Response. Parameters ---------- force_resp : array Array with","- percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x =","go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% -","and an input using Plotly. Parameters ---------- percentile : list, optional Sequence of",": array Time evolution of the state vector. nodes_list: array list with nodes","of freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly","axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, )","go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\"","must be between 0 and 100 inclusive. conf_interval : list, optional Sequence of","array. yout : array System response. xout : array Time evolution of the","(node, orientation angle). node : int indicate the node where the probe is","intervals to compute, which must be between 0 and 100 inclusive. kwargs :",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p,","natural frequency. Two options for plooting are available: Matplotlib and Bokeh. The user","\"\"\"Plot orbit response (3D). This function plots orbits for each node on the","name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\"","hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p","Diagram. It's possible to visualize multiples harmonics in a single plot to check","= np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p","for each frequency. number_dof = int Number of degrees of freedom per shaft","Time array. yout : array System response. xout : array Time evolution of","chooses between them using the attribute plot_type. The default is bokeh Parameters ----------","fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This","go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1}","color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\"","plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more","subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] )","= p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0),","100 inclusive. kwargs : optional Additional key word arguments can be passed to","mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n]","opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" +","range in rad/s. magnitude : array Array with the frequencies, magnitude (dB) of","= np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 -","indicate the node where the probe is located. orientation : float, probe orientation","inclusive. kwargs : optional Additional key word arguments can be passed to change","r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p),","go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile:",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6,","go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i +","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p),","fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1,","showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic",") for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p /","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot","nodes_list: array list with nodes from a rotor model. nodes_pos: array Rotor nodes","mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval:","np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig","method plots the time response given a tuple of probes with their nodes","2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar(","**kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[],","): \"\"\"Plot polar forced response using Plotly. Parameters ---------- dof : int Degree","frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to",") for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p /","graph_objects.Figure() The figure object with the plot. units : str, optional Unit system","for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2,","color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p","forced response using Plotly. Parameters ---------- dof : int Degree of freedom. units",") return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for Forced","can be passed to change the plot layout (e.g. width=800, height=600, ...). *See",") ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode(","2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter(","0 color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :,","{i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=(","bokeh Parameters ---------- speed_range : array Array with the speed range in rad/s.","\"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec","the plot. kwargs : optional Additional key word arguments can be passed to","**kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p,","fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar","width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ),","p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False,","- confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\")","method plots the unbalance response magnitude. Parameters ---------- probe : list of tuples","col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\",","probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0,","\"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):","np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean","{}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\"","name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs,","in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar(",": list, optional Sequence of confidence intervals to compute, which must be between","self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /","in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase","axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5,","- Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace(","kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace(","100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units","data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis,","y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] =","legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j,","the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams","- confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\")","phase : array Phase of the frequency response for node for each frequency.","borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle","\"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None: fig","%{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor","= self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots(","magnitude given an output and an input using Plotly. Parameters ---------- percentile :","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and","fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return","optional List with the harmonics to be plotted. The default is to plot","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\",","subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout(","interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ),","returns graphs for each type of analyses in st_rotor_assembly.py. \"\"\" import numpy as",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6,","= np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[...,","------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" conf_interval","- np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j,","theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase:","= number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot","+ _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range,","np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])),","percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2,","conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude","axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y -","is None: fig = go.Figure() color_p = 0 color_i = 0 for i,","int Number of degrees of freedom per shaft element's node Returns ------- fig","= p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), +","list, optional Sequence of confidence intervals to compute, which must be between 0","axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1,","which must be between 0 and 100 inclusive. harmonics: list, optional List withe","node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node],","y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude:","+ \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args,","+ p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2,","opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"","the time response given a tuple of probes with their nodes and orientations.","), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram.","plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This function","+ \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"),","legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]):","Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\"","0 and 100 inclusive. harmonics: list, optional List withe the harmonics to be","{}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range,","legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =","confidence intervals to compute, which must be between 0 and 100 inclusive. kwargs","freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure","= go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency:","conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}],","pair input/output. phase : array Array with the frequencies, phase of the frequency","), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec","for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0),","- p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j],","100 inclusive. harmonics: list, optional List withe the harmonics to be plotted. The",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if fig is","height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode(","title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\",","j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0)","evolution of the state vector. nodes_list: array list with nodes from a rotor","stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range : array","Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\")","2D view. Parameters ---------- node : int Select the node to display the","interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\",","self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0,","Frequency Response. Parameters ---------- speed_range : array Array with the speed range in","# fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] )","arguments can be passed to change the plot layout (e.g. width=800, height=600, ...).","p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50","\"\"\"Plot frequency response. This method plots the frequency and phase response given a","), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for","fmt: on for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","**kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs,","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20),","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",",") for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d(","Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which must","for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p,","the respective orbit response. percentile : list, optional Sequence of percentiles to compute,","- confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", )","{p}%\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs,","p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"),","row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900,","= self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,","x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\"","50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1],","output and an input. Parameters ---------- percentile : list, optional Sequence of percentiles","axes kwargs : optional Additional key word arguments can be passed to change","n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\",","with the plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range","color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False,","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for","np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x =","j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j]","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p,","fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]),","object with the plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\"","2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter(","x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\",","and 100 inclusive. harmonics: list, optional List withe the harmonics to be plotted.","height=800, ...). *See Plotly Python Figure Reference for more information. Returns ------- fig","= dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval,","{\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt: on for data in","rad/s. magnitude : array Array with the frequencies, magnitude (dB) of the frequency","+ 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude:","is bokeh Parameters ---------- speed_range : array Array with the speed range in",") fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"),","%{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"),","polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2,","def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This","their nodes and orientations. Parameters ---------- probe : list of tuples List with","ndof * n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p),","units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\":","provide plots for Frequency Response. Parameters ---------- speed_range : array Array with the","data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1)","* node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3,","fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1)","Parameters ---------- node : int Select the node to display the respective orbit","p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1),","{}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) )","p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p","v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d(","fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method plots",") for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],","np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif","default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[...,","*args, **kwargs ): \"\"\"Plot time response. This method plots the time response given","The default is to plot 1x. kwargs : optional Additional key word arguments","wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self,","be between 0% and 100% inclusive. units : str, optional Unit system Default","yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof","Select the node to display the respective orbit response. percentile : list, optional","conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This function plots orbits for","self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[],","(μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots","enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase,","dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] *","theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude:","np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,","the rotor system in a 3D view. Parameters ---------- percentile : list, optional","mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle))","line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1}","------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle","plooting are available: Matplotlib and Bokeh. The user chooses between them using the","default is to plot 1x. kwargs : optional Additional key word arguments can",") x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,","px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide plots for Campbell Diagram. It's","ndof * node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof","customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe","axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase,","\"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =","**kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- dof : int","units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{},","+ p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50","the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for","+ 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i +=","response. This method plots the phase response given an output and an input","/ 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace(","Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store","50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p /","- percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), )","ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0),",") return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This","node Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot.","*See Plotly Python Figure Reference for more information. Returns ------- fig : Plotly","a single plot to check other speeds which also excite a specific natural","z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if","**kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile :","row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis,","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14),","legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20),","def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using","phase response given an output and an input. Parameters ---------- percentile : list,","nodes and orientations. Parameters ---------- probe : list of tuples List with tuples","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic","percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This function plots orbits","%{y:.2e}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p","tuple of probes with their nodes and orientations. Parameters ---------- probe : list","array Time array. yout : array System response. xout : array Time evolution","Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" fig0 =","= np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0]","{i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i","opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False,","name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ),","amplitude vs frequency phase angle vs frequency. \"\"\" # fmt: off fig0 =","of degrees of freedom per shaft element's node Returns ------- fig : Plotly","self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig","Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self,","dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd","color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position:","), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot","and phase response given a set of probes. Parameters ---------- dof : int","axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase =","mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ),","opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i,","percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range,","node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node","\"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v","import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default","inclusive. args: optional harmonics : list, optional List with the harmonics to be","1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","results and provide plots for Frequency Response. Parameters ---------- speed_range : array Array","linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","output and an input using Plotly. Parameters ---------- percentile : list, optional Sequence","y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n in","legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes(","frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude","about the shaft. The 0 refers to +X direction. percentile : list, optional","[{}, None]] ) # fmt: on for data in fig0[\"data\"]: data.showlegend = False","units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label =","return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for Forced Response.","name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal","response given a set of probes. Parameters ---------- probe : list of tuples","hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range,","**kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 =","v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace(","v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots =","p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50","percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) )","Plotly figure with diagrams for frequency and log dec. \"\"\" def __init__(self, speed_range,","/ 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=(","axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval:","legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p in","+= 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 =","from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set","figure with diagrams for frequency and log dec. \"\"\" def __init__(self, speed_range, wd,","for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5,","), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\"","legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" +","<reponame>JuliaMota/ross<filename>ross/stochastic/st_results.py<gh_stars>0 \"\"\"STOCHASTIC ROSS plotting module. This module returns graphs for each type of","def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs frequency.","np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])),","which must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The","Parameters ---------- speed_range : array Array with the speed range in rad/s. wd","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" default_values =","ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0),","must be between 0 and 100 inclusive. units : str, optional Unit system","+ 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","as px from plotly import graph_objects as go from plotly import io as","h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"),","50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node +","0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j,","plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly.","list, optional Sequence of percentiles to compute, which must be between 0 and","False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1,","frequencies. magnitude : array Magnitude of the frequency response for node for each","enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n],","2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter(","- Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency:","axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5,","force response for each node for each frequency. frequency_range : array Array with","self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response. This method","response (3D). This function plots orbits for each node on the rotor system","x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"),","{}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs,","Array with the force response for each node for each frequency. frequency_range :","This method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of","make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]:","Plotly figure with diagrams for frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile,","Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" # fmt:","x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50","axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i","fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return","to +X direction. percentile : list, optional Sequence of percentiles to compute, which","p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i","{i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase:","fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1}","= np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) #","fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig =","subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for Time Response and","to compute, which must be between 0% and 100% inclusive. units : str,","data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2)","array Array with the frequencies, magnitude (dB) of the frequency response for each","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,","---------- speed_range : array Array with the speed range in rad/s. wd :","(μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None: fig =","interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\",","number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time","in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof +","Parameters ---------- force_resp : array Array with the force response for each node","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile:","phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range","color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs,","fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ):","opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","= nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args,","+ 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else","for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1,","if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\":","+ \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in","percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase:","subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout(","\"\"\"Store stochastic results and provide plots for Forced Response. Parameters ---------- force_resp :","for each type of analyses in st_rotor_assembly.py. \"\"\" import numpy as np from","dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k,","fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label,","- angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]),","p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p),","borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell","percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range,","%{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x,","response given a set of probes. Parameters ---------- dof : int Degree of","color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i,","angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0),","i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] *",") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0),","{p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return","i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p","v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"),","between 0 and 100 inclusive. args: optional harmonics : list, optional List with","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node,","figure object with the plot. units : str Magnitude unit system. Default is","np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p","0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:,","percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs frequency. Parameters ----------","%{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor","probes. Parameters ---------- dof : int Degree of freedom to observe the response.","p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","speed_range : array Array with the speed range in rad/s. wd : array","legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1],","x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} -","%{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j,","_probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp,","j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j","axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" +","with amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude,","def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response.","plot 1x. kwargs : optional Additional key word arguments can be passed to","with the plot. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs",") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node],","go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node +",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1),","be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object","** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs","j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5,","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","/ 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace(","p[0] * self.number_dof + 1 angle = p[1] # fmt: off operator =","in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof *","\"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,","def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response.","- percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x =","{}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\",","= log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies","{i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i","{p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", ) ) color_i +=","np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p","%{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile):","hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range,","p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile:","graph_objects.Figure() The figure object with the plot. units : str Magnitude unit system.","self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\":","/ 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3","*args, **kwargs): \"\"\"Plot orbit response (2D). This function plots orbits for a given","/ 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4","self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data,","= int Number of degrees of freedom per shaft element's node. Returns -------","), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof","node where the probe is located. orientation : float, probe orientation angle about","line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i,","(dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig =","probes. Parameters ---------- probe : list of tuples List with tuples (node, orientation","enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1}","line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for","xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout","fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i","+ p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1],","object with the plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval =","legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ),","fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if units","{}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs,","conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1,","p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True","\"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp,","graph_objects.Figure() The figure object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile =","palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic","p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2","subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return","col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14),","must be between 0 and 100 inclusive. harmonics: list, optional List withe the","v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace(","default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile,","with the harmonics to be plotted. The default is to plot 1x. kwargs","**kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1","x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p,","showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y -","**kwargs): \"\"\"Plot orbit response (3D). This function plots orbits for each node on","go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} -","frequency phase angle vs frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile,","\"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k,","for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams","shaft. The 0 refers to +X direction. percentile : list, optional Sequence of","%{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace(","percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is","50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p),","1-dimensional array Time array. yout : array System response. xout : array Time","interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\",","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i +","go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency:","i < 0 else i for i in aux_phase] ) angle = p[1]","= np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3,","+ 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence","axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=(","= self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2","amplitude vs frequency phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units,","...). *See Plotly Python Figure Reference for more information. Returns ------- fig :","conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method plots the phase response given","y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p,","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs):","list with nodes from a rotor model. nodes_pos: array Rotor nodes axial positions.","be between 0 and 100 inclusive. harmonics: list, optional List withe the harmonics","**kwargs): \"\"\"Plot phase angle response. This method plots the phase response given an",": Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional Additional","= 0 color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:,","enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"),","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,","plots the frequency and phase response given a set of probes. Parameters ----------","line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency:","r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i +","attribute plot_type. The default is bokeh Parameters ---------- speed_range : array Array with","self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos","stochastic results and provide plots for Campbell Diagram. It's possible to visualize multiples","conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{},","= go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter(","z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list):","%{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode","default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2)","+ p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2,","x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j),",": array Array with the force response for each node for each frequency.","be between 0 and 100 inclusive. kwargs : optional Additional key word arguments","def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec =",") probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp =","\"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in","p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\",","frequency response magnitude given an output and an input using Plotly. Parameters ----------","np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp","Default is \"mic-pk-pk\" kwargs : optional Additional key word arguments can be passed","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1),","+ 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1)","fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] )","for each frequency. frequency_range : array Array with the frequencies. magnitude : array","= \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile","{i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency:","to compute, which must be between 0% and 100% inclusive. fig : Plotly","io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default =","== \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if","in st_rotor_assembly.py. \"\"\" import numpy as np from plotly import express as px","), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency","vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp","return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot","): \"\"\"Plot frequency response. This method plots the unbalance response magnitude. Parameters ----------","ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for","frequency. Two options for plooting are available: Matplotlib and Bokeh. The user chooses","= p[0] * self.number_dof + 1 angle = p[1] # fmt: off operator","\"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace(","fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots","color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe","Sequence of confidence intervals to compute, which must be between 0% and 100%","+ 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence","plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This","< 0 else i for i in aux_phase] ) angle = p[1] probe_phase[i]","node : int indicate the node where the probe is located. orientation :","height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self,","off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval,","fig = go.Figure() color_p = 0 color_i = 0 for i, p in","fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for","\"\"\" if fig is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval =","a specific natural frequency. Two options for plooting are available: Matplotlib and Bokeh.","given an output and an input using Plotly. Parameters ---------- percentile : list,","frequency. number_dof = int Number of degrees of freedom per shaft element's node.","Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict(","with the frequencies. magnitude : array Magnitude of the frequency response for node","color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x","phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values =","\"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval =","---------- percentile : list, optional Sequence of percentiles to compute, which must be","plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This function plots","= \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for","wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped","fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data","fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i","= np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","Parameters ---------- dof : int Degree of freedom to observe the response. percentile","using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","visualize multiples harmonics in a single plot to check other speeds which also","line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\",","+ 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in","in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j],","vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for","units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval,","node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency","self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 =","enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase =","for node for each frequency. phase : array Phase of the frequency response","the plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile","== \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude","natural frequencies log_dec : array Array with the Logarithmic decrement Returns ------- subplots","= go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency:","None: fig = go.Figure() color_i = 0 color_p = 0 for i, p","magnitude plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units","p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[...,","= np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units ==","x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0),","of freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The","node + 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof","color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x","x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1}","2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter(",": int indicate the node where the probe is located. orientation : float,","The figure object with the plot. args : optional Additional plot axes kwargs","y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof","plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,","inclusive. conf_interval : list, optional Sequence of confidence intervals to compute, which must","Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i,","title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic","row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ),","Parameters ---------- time_range : 1-dimensional array Time array. yout : array System response.","with the frequencies, phase of the frequency response for each pair input/output. Returns",": int Degree of freedom to observe the response. percentile : list, optional",") return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for Frequency","using Plotly. Parameters ---------- probe : list of tuples List with tuples (node,","and Bokeh. The user chooses between them using the attribute plot_type. The default","x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50","Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and","for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2","orientation : float, probe orientation angle about the shaft. The 0 refers to","# fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase):","{}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\",","np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p","in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6,","method plots the frequency and phase response given a set of probes. Parameters","col=1) for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data","Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900,","self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters","List with tuples (node, orientation angle). node : int indicate the node where","---------- force_resp : array Array with the force response for each node for","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval","enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i),","operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:,","for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range,","in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p /","percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile :","\"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):","opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude:","i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in","def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This","confidence intervals to compute, which must be between 0 and 100 inclusive. args:","p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p),","self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p /","hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig","p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\",","with amplitude vs frequency phase angle vs frequency. \"\"\" # fmt: off fig0","degrees of freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots()","self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof","percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure()","ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof *","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile:","linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p","= np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :,","1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,","figure with amplitude vs frequency phase angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile,","percentiles to compute, which must be between 0 and 100 inclusive. conf_interval :","100% inclusive. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs :","color_p = 0 color_i = 0 for i, p in enumerate(probe): probe_phase =","go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range,","%{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(percentile):","50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p /","more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for","radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ),",") return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar","{i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","plotted. The default is to plot 1x. kwargs : optional Additional key word","plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile =","self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None,","- percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\",",") ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean","import graph_objects as go from plotly import io as pio from plotly.subplots import","Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure","response for each pair input/output. phase : array Array with the frequencies, phase","of the state vector. nodes_list: array list with nodes from a rotor model.","line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p in","{p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1]))","axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile:","a set of probes. Parameters ---------- dof : int Degree of freedom to","**kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for","------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" def","fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2)","This method plots the frequency and phase response given a set of probes.","interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\")","= phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi","n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6,","Degree of freedom to observe the response. percentile : list, optional Sequence of","p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\",","enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5,","if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node],","be passed to change the plot layout (e.g. width=800, height=600, ...). *See Plotly","dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k,","bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide","node on the rotor system in a 3D view. Parameters ---------- percentile :","opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y -","axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase,","col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1,","/ 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i],","go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\",","legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p","phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self,","* n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]),","default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h","Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude -","fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis,","st_rotor_assembly.py. \"\"\" import numpy as np from plotly import express as px from","opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i + 1}","time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos","conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile : list,","size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return","y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency:","j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h),","is \"mic-pk-pk\". kwargs : optional Additional key word arguments can be passed to","* np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt:","), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 -","be plotted. The default is to plot 1x. kwargs : optional Additional key","0 and 100 inclusive. units : str, optional Unit system Default is \"mic-pk-pk\"","mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50","angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude","go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1],","plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig","for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] =","/ 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\",","Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return","y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} -","angle vs frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units,","in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof *","row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis,","* node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1,","go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe","v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase,","probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method plots the phase","angle about the shaft. The 0 refers to +X direction. percentile : list,","@ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 +","p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof","List with the harmonics to be plotted. The default is to plot 1x.","ROSS plotting module. This module returns graphs for each type of analyses in","1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[],","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20),","- Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","+= 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def","in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis,","confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", ) )","{i + 1} - confidence interval: {p}%\", ) ) color_i += 1 fig.update_layout(","np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i =","np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,","hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) )","axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe","subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict(","v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line","conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This function plots orbits for","can be passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See",") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6,","frequency response. This method plots the frequency and phase response given a set","def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase =","\"\"\"Plot polar forced response using Plotly. Parameters ---------- probe : list of tuples","units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\"","%{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with","plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This method","x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" +","legendgroup=\"mean\", showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" +","p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j","** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase =","borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots","else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig","vs frequency phase angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range,","+ 1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), -","ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j",") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6,","+ \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar(","col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict(","name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log","dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label =","for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1)","plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range","compute, which must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure()","= np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 +","(_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0),","an output and an input using Plotly. Parameters ---------- percentile : list, optional","Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh","to compute, which must be between 0 and 100 inclusive. args: optional harmonics","\"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p +=","class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for Frequency Response. Parameters ----------","color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ),","+ \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1","confidence interval: {p}%\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"),","+ 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\"","+ \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p",") fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h,","%{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This method plots the frequency","if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos,","Campbell Diagram. It's possible to visualize multiples harmonics in a single plot to","return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D).","**kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig","title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped","i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","{p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", )","yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig class","for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time","dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle)","plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile","), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i, p in","\"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase","%{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20),","graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" #","for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x","system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional key word arguments can be","= np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 +","array Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly","= self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\",","= 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for","Array with the frequencies, magnitude (dB) of the frequency response for each pair","axes with magnitude plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\"","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs):",":, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof]","__init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout","\"\"\"Plot frequency response. This method plots the frequency and phase response given an","go.Figure() color_i = 0 color_p = 0 for i, p in enumerate(probe): dofx","and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot.","Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for","vector. nodes_list: array list with nodes from a rotor model. nodes_pos: array Rotor","y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","method plots the frequency and phase response given an output and an input.","vs frequency phase angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range","figure with amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, forced_resp,","fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency:","color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"),","with the speed range in rad/s. magnitude : array Array with the frequencies,","color_i = 0 color_p = 0 for i, p in enumerate(probe): dofx =","response for node for each frequency. phase : array Phase of the frequency","+ 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval:","probes with their nodes and orientations. Parameters ---------- probe : list of tuples","if fig is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval)","\"\"\"Store stochastic results and provide plots for Campbell Diagram. It's possible to visualize","n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode( self,","default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos))","= self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots(",") fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes(","name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs,","node : int Select the node to display the respective orbit response. percentile","damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of","the speed range in rad/s. magnitude : array Array with the frequencies, magnitude","{p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs),","+ \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range,","np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase","%{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval):","fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, )","- Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout(","inclusive. units : str, optional Unit system Default is \"mic-pk-pk\" kwargs : optional",": array Magnitude of the frequency response for node for each frequency. phase","return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot","percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response. This method plots the","i for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] -",") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p","*args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1","hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, )","p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2,","go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j],","50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]),","line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\",","0 and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals to","2 * np.pi if i < 0 else i for i in aux_phase]",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" def __init__(self, time_range,","optional Additional key word arguments can be passed to change the plot (e.g.","which must be between 0 and 100 inclusive. units : str, optional Unit","data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2)","frequency response. This method plots the unbalance response magnitude. Parameters ---------- probe :","**kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence","with diagrams for frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args,","- Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) )","**kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- probe : list","return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for Frequency Response.","def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots the","p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]),","ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set Plotly palette of colors colors1","cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for data in fig0[\"data\"]:","p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1)","response. This method plots the frequency and phase response given an output and","for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1,","function plots orbits for a given node on the rotor system in a","fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0),","Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object","dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) **","fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs,","fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i),","= np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i","the frequency and phase response given a set of probes. Parameters ---------- dof","= \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else:","100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs","= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if","[[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for","frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]),","plotly import graph_objects as go from plotly import io as pio from plotly.subplots","\"\"\"Plot polar forced response using Plotly. Parameters ---------- dof : int Degree of","in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j +","the unbalance response magnitude. Parameters ---------- probe : list of tuples List with","opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i +","the plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range =","np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace(","fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i),","* n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]),","---------- speed_range : array Array with the speed range in rad/s. magnitude :","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[],","input using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to","go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i +","conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- dof","of the frequency response for node for each frequency. phase : array Phase","with the plot. \"\"\" ndof = self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval)","i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]),","object with the plot. \"\"\" if fig is None: fig = go.Figure() default_values","frequency. frequency_range : array Array with the frequencies. magnitude : array Magnitude of","= \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval","confidence intervals to compute, which must be between 0% and 100% inclusive. fig","+ \"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, )","intervals to compute, which must be between 0 and 100 inclusive. harmonics: list,","default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\":","the frequency response for node for each frequency. phase : array Phase of","(μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k,","Sequence of percentiles to compute, which must be between 0 and 100 inclusive.","for data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in",": Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\" if units ==","each node for each frequency. frequency_range : array Array with the frequencies. magnitude","col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\",","= go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node","in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line =","go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]),","self.number_dof default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v","**kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1","speeds which also excite a specific natural frequency. Two options for plooting are","str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional key word","for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude","the node to display the respective orbit response. percentile : list, optional Sequence","line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude:","\"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node","axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j ==","def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method plots the","for Forced Response. Parameters ---------- force_resp : array Array with the force response","number_dof : int Number of degrees of freedom per shaft element's node Returns","probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response. This method plots","name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","intervals to compute, which must be between 0 and 100 inclusive. fig :",") color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50","%{y:.2f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase,","the harmonics to be plotted. The default is to plot 1x. kwargs :","and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False)",": str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional Additional key","= np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units","+ 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1","phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency.","* n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True","self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ):","= np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 +","row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), ) return fig","= np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range *",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1),","col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ),",") return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This","None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof","= frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\",","go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i","fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1,","Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","+ \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5,","v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1),","default is bokeh Parameters ---------- speed_range : array Array with the speed range","fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency:","inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs :","- np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j,","Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d(","name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ),","v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1),",") fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2","+ 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) )","data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis,","{}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1","yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout =","inclusive. harmonics: list, optional List withe the harmonics to be plotted. The default",") ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X -","information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency","\"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile):","fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1)","go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe): probe_phase","Array with the speed range in rad/s. wd : array Array with the","plots the phase response given a set of probes. Parameters ---------- probe :","percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (3D). This function plots orbits","\"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label","x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0),","%{customdata:.2f}</b>\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p,","with nodes from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof","enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2,","with the plot. args : optional Additional plot axes kwargs : optional Additional","interval: {p}%\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ),","for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2,","**kwargs ): \"\"\"Plot time response. This method plots the time response given a","\"\"\" import numpy as np from plotly import express as px from plotly","list, optional Sequence of confidence intervals to compute, which must be between 0%","col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\",","confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude:","legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p in","p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence","x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j),","probe : list of tuples List with tuples (node, orientation angle). node :","figure object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if","def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell","confidence intervals to compute, which must be between 0 and 100 inclusive. fig","\"\"\"Plot orbit response (2D). This function plots orbits for a given node on","%{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for i,","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store","data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2)","50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1,","2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p","np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0])","with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units ==","wd : array Array with the damped natural frequencies log_dec : array Array","*See Plotly Python Figure Reference for more information. Returns ------- subplots : Plotly","which also excite a specific natural frequency. Two options for plooting are available:","response magnitude. Parameters ---------- probe : list of tuples List with tuples (node,","- p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1,","fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1)","The figure object with the plot. \"\"\" if units == \"m\": y_axis_label =","is \"mic-pk-pk\" kwargs : optional Additional key word arguments can be passed to",": array Array with the frequencies, magnitude (dB) of the frequency response for","fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace(","percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", )","import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set Plotly palette","name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\"","= np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2 =","color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1","phase angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp","subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2,","), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof","confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase:","between 0 and 100 inclusive. units : str, optional Unit system Default is","ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for Forced Response. Parameters ---------- force_resp","+ 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\",","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1],","# set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class","@ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2","------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" default_values","withe the harmonics to be plotted. The default is to plot 1x. kwargs","Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for","self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i < 0","p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2","for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2,","p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]),","enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node","plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors","j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\",","** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter(","line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\"","= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in","1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs):","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range,","graph_objects.Figure() The figure object with the plot. args : optional Additional plot axes","log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if units == \"m\":","bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide","ndof * n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5,","= make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) #","%{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp,","1} - confidence interval: {p}%\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label,","color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in","for plooting are available: Matplotlib and Bokeh. The user chooses between them using","i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0),","%{customdata:.2f}</b>\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude,","%{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x,","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]),","): \"\"\"Plot time response. This method plots the time response given a tuple","Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d(","* node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof *",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *","color_p = 0 color_i = 0 for i, p in enumerate(probe): dofx =","Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200,","is located. orientation : float, probe orientation angle about the shaft. The 0","the probe is located. orientation : float, probe orientation angle about the shaft.","polar_kwargs : optional Additional key word arguments can be passed to change the","np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on","/ 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace(","self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof","np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp","axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1,","node on the rotor system in a 2D view. Parameters ---------- node :","\"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values =","key word arguments can be passed to change the plot layout only (e.g.","and 100 inclusive. args: optional harmonics : list, optional List with the harmonics","name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(","p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p in enumerate(percentile):","%{y:.2e}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude,","units : str, optional Unit system Default is \"mic-pk-pk\". kwargs : optional Additional","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p),","object with the plot. \"\"\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos):","**kwargs, ): \"\"\"Plot amplitude vs frequency. This method plots the frequency response magnitude","fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i","the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles","Rotor nodes axial positions. number_dof : int Number of degrees of freedom per","showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X","nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs","elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude","of probes. Parameters ---------- dof : int Degree of freedom to observe the","title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900,","figure object with the plot. units : str, optional Unit system Default is","col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), )","== \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude","Unit system Default is \"mic-pk-pk\" kwargs : optional Additional key word arguments can","name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\"",") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0),","speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs):","if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label","h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3,","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range,",": Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency.","name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude:","fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the frequency and","i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1)","The 0 refers to +X direction. percentile : list, optional Sequence of percentiles","fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D). This function plots orbits for a","go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1],","which must be between 0 and 100 inclusive. kwargs : optional Additional key","= go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"),","np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p","in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6,","v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy =","enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j,",": float, probe orientation angle about the shaft. The 0 refers to +X","fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\" if units","colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and","%{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1 for j, p","word arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0,","\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\",","- confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency:","------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" ndof","i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor","probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[],","which must be between 0 and 100 inclusive. conf_interval : list, optional Sequence","orientation angle about the shaft. The 0 refers to +X direction. percentile :","0 and 100 inclusive. args: optional harmonics : list, optional List with the","confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\")","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" conf_interval =","make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for data","/ 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p","phase response given a set of probes. Parameters ---------- dof : int Degree","linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot","go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe): dofx","of freedom. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs :","Default is \"mic-pk-pk\". kwargs : optional Additional key word arguments can be passed","legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j,","fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line,","go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]),","which must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict(","y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\"","frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k,",") ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14),","subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs","conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile,","pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v)","\"\"\"Plot time response. This method plots the time response given a tuple of","to compute, which must be between 0 and 100 inclusive. fig : Plotly","Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return","- Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude","It's possible to visualize multiples harmonics in a single plot to check other","* np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0),","{i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p","angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase,","single plot to check other speeds which also excite a specific natural frequency.","Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The","if i < 0 else i for i in aux_phase] ) angle =","opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X -","title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200,","/ 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4","fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} -","figure with amplitude vs frequency phase angle vs frequency. \"\"\" # fmt: off","opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=(","------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log","magnitude. Parameters ---------- probe : list of tuples List with tuples (node, orientation","from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int","/ 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p","0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i]","- Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j,","time response given a tuple of probes with their nodes and orientations. Parameters","fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"),","enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j,","- Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in enumerate(percentile): fig.add_trace(","(s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot","z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]),","opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof *","i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]),","**kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\",","* n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0,","2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p","\"rowspan\": 2}], [{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for","y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval)","vs frequency phase angle vs frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe,","+ 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency:","p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i),","the frequencies, phase of the frequency response for each pair input/output. Returns -------","array Magnitude of the frequency response for node for each frequency. phase :","the frequencies, magnitude (dB) of the frequency response for each pair input/output. phase","probe_phase[i] = np.array( [i + 2 * np.pi if i < 0 else","array Rotor nodes axial positions. number_dof : int Number of degrees of freedom","opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, )","50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50","numpy as np from plotly import express as px from plotly import graph_objects","%{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i,","the plot. \"\"\" if fig is None: fig = go.Figure() default_values = dict(mode=\"lines\")","specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data,","and 100% inclusive. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs","/ 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace(","p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 -","1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1","\"Phase: %{y:.2f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,","kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for","+= 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None,","plot. args : optional Additional plot axes kwargs : optional Additional key word","go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\",","p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X","percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0","): \"\"\"Plot polar forced response using Plotly. Parameters ---------- probe : list of","in a 2D view. Parameters ---------- node : int Select the node to","This function plots orbits for a given node on the rotor system in","= magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs, ):","np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,","p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2","plots for Frequency Response. Parameters ---------- speed_range : array Array with the speed","= np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3 =","for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5,","amplitude vs frequency. This method plots the frequency response magnitude given an output","in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe","name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency:","axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position:","width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig","for each pair input/output. phase : array Array with the frequencies, phase of","go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase:","confidence intervals to compute, which must be between 0 and 100 inclusive. harmonics:","self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k,","= np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", )","be between 0 and 100 inclusive. conf_interval : list, optional Sequence of confidence","x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50","Matplotlib and Bokeh. The user chooses between them using the attribute plot_type. The","in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0,","with the plot. kwargs : optional Additional key word arguments can be passed","bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs,",") color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return","for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5),","**kwargs, ): \"\"\"Plot frequency response. This method plots the frequency and phase response","go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} -","1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" +","compute, which must be between 0 and 100 inclusive. kwargs : optional Additional","passed to change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python","log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the damped natural frequencies vs","legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" +","subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1)","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if fig is None:","np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe):",") color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self,","opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns ------- fig","orientations. Parameters ---------- probe : list of tuples List with tuples (node, orientation","self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n","self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None,","line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position:","compute, which must be between 0% and 100% inclusive. units : str, optional","This function plots orbits for each node on the rotor system in a","Bokeh plot axes with magnitude plot. \"\"\" if units == \"m\": y_axis_label =","log_dec : array Array with the Logarithmic decrement Returns ------- subplots : Plotly","color_p = 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof","using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" +","between 0 and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals","more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs","fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j),","{}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\",","line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i,","name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14),","\"\"\"Store stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range :","- Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1","and an input using bokeh. Parameters ---------- percentile : list, optional Sequence of",": int Number of degrees of freedom per shaft element's node Returns -------","opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i +","and 100 inclusive. kwargs : optional Additional key word arguments can be passed","on the rotor system in a 3D view. Parameters ---------- percentile : list,","arguments can be passed to change the plot layout only (e.g. width=1000, height=800,","with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is","radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[],","and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot.","for each frequency. phase : array Phase of the frequency response for node","self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /","Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs,","exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig def","plots for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array","**kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude","_probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle))","frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs,","make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt:","p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\",","fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout(",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node],","in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2,","self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def","color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0])","polar forced response using Plotly. Parameters ---------- probe : list of tuples List","magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self,","units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1","np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p","= forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof =",") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6,","given node on the rotor system in a 2D view. Parameters ---------- node","subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\",","diagrams for frequency and log dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range","the frequency response for each pair input/output. phase : array Array with the","borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic","mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] =","function plots orbits for each node on the rotor system in a 3D","kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\",","go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\"","axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase,","shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude","percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range,","height=600, ...). *See Plotly Python Figure Reference for more information. Returns ------- fig",") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6,","\"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig =","font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[],","= np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig","fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence","j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe",": Plotly graph_objects.Figure() The figure object with the plot. units : str Magnitude","y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\"","pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if","), ) ) color_p += 1 for j, p in enumerate(conf_interval): p1 =","with the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), ) return","fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=(","graph_objects.Figure() The figure object with the plot. \"\"\" if fig is None: fig","fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i),","frequency response. This method plots the frequency and phase response given an output","+ 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude:",": array Array with the speed range in rad/s. magnitude : array Array","fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs)","results and provide plots for Forced Response. Parameters ---------- force_resp : array Array",") for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p,","0 and 100 inclusive. kwargs : optional Additional key word arguments can be","plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\",","), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p,","), legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults:","2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i],","np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p","with the damped natural frequencies log_dec : array Array with the Logarithmic decrement","customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i","fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\",","Parameters ---------- dof : int Degree of freedom. units : str Magnitude unit","= xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d(","hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\",","and 100 inclusive. units : str, optional Unit system Default is \"mic-pk-pk\" kwargs","\"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False),","\"\"\"Plot frequency response. This method plots the unbalance response magnitude. Parameters ---------- probe","\"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","__init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase","units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the frequency and phase","y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\")","This method plots the phase response given an output and an input using","inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units :","input. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","ndof * node + 1], 50 + p / 2, axis=0) p4 =","time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout","plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig","go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1}","2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar(","color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p","data in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]:","data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]:","harmonics: list, optional List withe the harmonics to be plotted. The default is","np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p","mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1}","def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout =","return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method","polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5,","probe orientation angle about the shaft. The 0 refers to +X direction. percentile","x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[...,","rotor system in a 3D view. Parameters ---------- percentile : list, optional Sequence","return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method","2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter(","axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe","%{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j,","for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x,","ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"),","plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set Plotly",") x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase,","go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile:","row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict(","yout : array System response. xout : array Time evolution of the state","50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]),","units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\"","50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p),","fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]),","fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None,","be passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly","50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p /","plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method plots the phase","(m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label =","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" def __init__(self,","%{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[],","a 2D view. Parameters ---------- node : int Select the node to display","self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using","nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[],","+ \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(","intervals to compute, which must be between 0% and 100% inclusive. units :","nodes axial positions. number_dof : int Number of degrees of freedom per shaft","for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in","return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results and provide plots for Time Response","xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self,","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile","is None: fig = go.Figure() color_i = 0 color_p = 0 for i,","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i","y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" +","p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2,","2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5,","kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof","else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y","for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0),","{}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ),","axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval:","with amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude,","= np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node +","1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", )","between them using the attribute plot_type. The default is bokeh Parameters ---------- speed_range","fig : Plotly graph_objects.Figure() The figure object with the plot. units : str,","y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe","p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0)","y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time:","\"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of","array Array with the speed range in rad/s. wd : array Array with","%{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self,","probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3,","with their nodes and orientations. Parameters ---------- probe : list of tuples List","fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response (2D).","Plotly graph_objects.Figure() The figure object with the plot. args : optional Additional plot",") ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude -","array list with nodes from a rotor model. nodes_pos: array Rotor nodes axial","x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1),","- Mode {}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"),","magnitude : array Array with the frequencies, magnitude (dB) of the frequency response","in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] *","bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs):","+ 2 * np.pi if i < 0 else i for i in",": array Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots()","go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile(","and provide plots for Campbell Diagram. It's possible to visualize multiples harmonics in","graph_objects as go from plotly import io as pio from plotly.subplots import make_subplots","np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p","hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile):","plot axes kwargs : optional Additional key word arguments can be passed to","%{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1","system Default is \"mic-pk-pk\" kwargs : optional Additional key word arguments can be","(dB)</b>\" if fig is None: fig = go.Figure() color_p = 0 color_i =","gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\",","fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs):","optional Sequence of confidence intervals to compute, which must be between 0% and","50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p /","row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]:","fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs)","frequency response for each pair input/output. phase : array Array with the frequencies,","= np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :,","0 color_p = 0 for i, p in enumerate(probe): dofx = p[0] *","{p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1]))","colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide plots for Campbell","name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x =","node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])),","= np.array( [i + 2 * np.pi if i < 0 else i","---------- dof : int Degree of freedom. units : str Magnitude unit system.","subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1)","to compute, which must be between 0 and 100 inclusive. kwargs : optional","pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs","default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\",","probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator","v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data","self.number_dof + 1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle),","vs frequency. This method plots the frequency response magnitude given an output and","100 inclusive. args: optional harmonics : list, optional List with the harmonics to","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\",","system in a 2D view. Parameters ---------- node : int Select the node","%{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j],","[np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in","p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0)","None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]]","go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p),","%{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def","\"rowspan\": 2}], [{}, None]] ) # fmt: on for data in fig0[\"data\"]: data.showlegend","\"\"\"STOCHASTIC ROSS plotting module. This module returns graphs for each type of analyses","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","set of probes. Parameters ---------- probe : list of tuples List with tuples","legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for","percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0","width=800, height=600, ...). *See Plotly Python Figure Reference for more information. Returns -------","row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ),","p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase):","50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\",","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),",") # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i +","x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} -","in rad/s. magnitude : array Array with the frequencies, magnitude (dB) of the","y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence interval:",") ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in","opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns ------- subplots","= \"<b>Amplitude (dB)</b>\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure()",": list, optional List with the harmonics to be plotted. The default is","plot. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional","color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs,","angle vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False)","conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method plots the phase response given","self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs,","- angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i","self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1] # fmt:",") color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[],","= np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 -","p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50","*args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile","p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1)","np.array( [i + 2 * np.pi if i < 0 else i for","in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j +","height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self,","between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with","object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig","color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" +","dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle","xout : array Time evolution of the state vector. nodes_list: array list with","hoverinfo=\"none\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50","Unit system Default is \"mic-pk-pk\". kwargs : optional Additional key word arguments can","which must be between 0% and 100% inclusive. units : str, optional Unit","+ p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2,","fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n +","The figure object with the plot. kwargs : optional Additional key word arguments","provide plots for Campbell Diagram. It's possible to visualize multiples harmonics in a","1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for","forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase =","for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs)","y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe","= \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None:","fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response.","def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method plots","The figure object with the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","/ 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace(","the frequency and phase response given an output and an input. Parameters ----------","* np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0,","plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more","np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p","for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with","- percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x =","{p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) )","exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[],","of tuples List with tuples (node, orientation angle). node : int indicate the","probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\",","key word arguments can be passed to change the plot layout (e.g. width=800,","number_dof = int Number of degrees of freedom per shaft element's node. Returns","+ (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp,","0 else i for i in aux_phase] ) angle = p[1] probe_phase[i] =","2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 =","passed to change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly","axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p / 2,","= self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]:","subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data","p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j],","= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for",") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p","i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace(","np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0,","harmonics : list, optional List with the harmonics to be plotted. The default","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","units : str, optional Unit system Default is \"mic-pk-pk\" kwargs : optional Additional","x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[...,","2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 =","= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig","the damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence","Forced Response. Parameters ---------- force_resp : array Array with the force response for","frequency response. This method plots the phase response given a set of probes.","50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\",","= \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None:","using Plotly. Parameters ---------- dof : int Degree of freedom. units : str","x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\",","= go.Figure() color_i = 0 color_p = 0 for i, p in enumerate(probe):","Additional key word arguments can be passed to change the plot (e.g. line=dict(width=4.0,","= phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[],","( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 )","row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\",","specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{}, None]] ) # fmt: on for data","= \"browser\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 =","method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles","np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig","of probes with their nodes and orientations. Parameters ---------- probe : list of","np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i,","\"\"\"Plot amplitude vs frequency. This method plots the frequency response magnitude given an","import express as px from plotly import graph_objects as go from plotly import","bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot","p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]),","fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n","%{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p in enumerate(percentile): fig.add_trace(","The user chooses between them using the attribute plot_type. The default is bokeh","p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2","is to plot 1x. kwargs : optional Additional key word arguments can be","compute, which must be between 0 and 100 inclusive. harmonics: list, optional List","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p,",") ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace(","[i + 2 * np.pi if i < 0 else i for i","to be plotted. The default is to plot 1x. kwargs : optional Additional","p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" +","r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\"","intervals to compute, which must be between 0 and 100 inclusive. units :","color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns -------","options for plooting are available: Matplotlib and Bokeh. The user chooses between them","k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx =","enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0]","of probes. Parameters ---------- probe : list of tuples List with tuples (node,",": array Array with the frequencies, phase of the frequency response for each","self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method","graph_objects.Figure() The figure object with the plot. \"\"\" def __init__(self, time_range, yout, xout,","self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ):","for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig =","\"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\"","Array with the frequencies, phase of the frequency response for each pair input/output.","+ \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2,","name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for","* n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n","module returns graphs for each type of analyses in st_rotor_assembly.py. \"\"\" import numpy","response given a tuple of probes with their nodes and orientations. Parameters ----------","100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. args","in a single plot to check other speeds which also excite a specific","hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p in enumerate(percentile):","orbits for each node on the rotor system in a 3D view. Parameters","i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1)","opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\",","+ np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout):","log dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd =","in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for","gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig","orbit response (2D). This function plots orbits for a given node on the","+= 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[],","int Select the node to display the respective orbit response. percentile : list,","Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for","fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \"\"\"Plot frequency response. This method","- p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2,","conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ----------","fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\",","r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p),","axis=1), customdata=self.speed_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" +","Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\"",") fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\",","figure object with the plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude",": array Array with the damped natural frequencies log_dec : array Array with","100 inclusive. conf_interval : list, optional Sequence of confidence intervals to compute, which","in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: data.showlegend","axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p /","enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i),","confidence intervals to compute, which must be between 0% and 100% inclusive. units","Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\" if units == \"m\":","Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","str, optional Unit system Default is \"mic-pk-pk\" kwargs : optional Additional key word","\"mic-pk-pk\" kwargs : optional Additional key word arguments can be passed to change","(3D). This function plots orbits for each node on the rotor system in","\"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1 for j, p in enumerate(conf_interval):","if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label","node for each frequency. phase : array Phase of the frequency response for","conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response. This method plots the time","opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\",","{i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude:","{i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\",","axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p /","an output and an input. Parameters ---------- percentile : list, optional Sequence of","%{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p","in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 =","i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 +","None: fig = go.Figure() color_p = 0 color_i = 0 for i, p","= np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +","for each node for each frequency. frequency_range : array Array with the frequencies.","pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile","= self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items():","i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1)","plot to check other speeds which also excite a specific natural frequency. Two","plots orbits for each node on the rotor system in a 3D view.","object with the plot. units : str Magnitude unit system. Default is \"mic-pk-pk\"","np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j,","title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\",","== \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval","analyses in st_rotor_assembly.py. \"\"\" import numpy as np from plotly import express as","mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1}","pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" #","operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) **","observe the response. percentile : list, optional Sequence of percentiles to compute, which","+ \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","key word arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"),","conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None:","in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i +","Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase","system in a 3D view. Parameters ---------- percentile : list, optional Sequence of","number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list","col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis,","\"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for k, v in default_values.items():","for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\",","for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5,","method plots the frequency response magnitude given an output and an input using","speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def","The figure object with the plot. units : str, optional Unit system Default","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]),","Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(",") probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp =","{}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" +",") for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean -","on the rotor system in a 2D view. Parameters ---------- node : int","= operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) **","in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter(","{i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) )","{i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p +=","p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) )","1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1,","+ \"Frequency: %{y:.3f}\" ), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...)","response given an output and an input. Parameters ---------- percentile : list, optional","fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j,","p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p /","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof","+ p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 -","/ 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p /","axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} -","= go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range,","on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i +","theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} -","default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in","array Phase of the frequency response for node for each frequency. number_dof =","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16),","fig : Plotly graph_objects.Figure() The figure object with the plot. args : optional","frequency phase angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof):",") ) color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,","Plotly. Parameters ---------- dof : int Degree of freedom. units : str Magnitude","v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for","phase angle response. This method plots the phase response given an output and","): \"\"\"Plot frequency response. This method plots the frequency and phase response given","fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot( self,","bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot","in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 =","angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i +","fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y","args: optional harmonics : list, optional List with the harmonics to be plotted.","title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ),","* node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof *","p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50","= speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1],","# fmt: on for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data, row=1, col=1)","decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200,","p, axis=0), opacity=1.0, name=\"percentile: {}%\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0","* node + 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude:","units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ---------- probe :","fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1}","the damped natural frequencies log_dec : array Array with the Logarithmic decrement Returns","a tuple of probes with their nodes and orientations. Parameters ---------- probe :","{p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1]))","figure with amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, speed_range,","Plotly Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure()","np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1),","for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx","array Array with the speed range in rad/s. magnitude : array Array with","+ 1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" +","units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot amplitude vs frequency. This method plots the frequency response","provide plots for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional","None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)","vs frequency. \"\"\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for","np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1]","as np from plotly import express as px from plotly import graph_objects as","...) *See Plotly Python Figure Reference for more information. Returns ------- fig :","i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range,","axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,","class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for Forced Response. Parameters ----------","= np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 -","y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p,","axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True","fig is None: fig = go.Figure() color_p = 0 color_i = 0 for","fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2, axis=0) p4","\"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ","ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=(","to change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python","= time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos =","graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\" if units == \"m\": y_axis_label","conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure()","ndof * node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter(","fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1}","v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile,","fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\":","This method plots the frequency and phase response given an output and an",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" default_values = dict(mode=\"lines\")","harmonics in a single plot to check other speeds which also excite a","- X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults:","unbalance response magnitude. Parameters ---------- probe : list of tuples List with tuples","= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x","plot_type. The default is bokeh Parameters ---------- speed_range : array Array with the","go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\"","np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0])","False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y -","------- fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if","color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe,",") return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ):","= 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy","the frequencies. magnitude : array Magnitude of the frequency response for node for","= np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 -","frequency and log dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range","name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, )","px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide plots for","probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator","the attribute plot_type. The default is bokeh Parameters ---------- speed_range : array Array","name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes(","fig : Plotly graph_objects.Figure() The figure object with the plot. units : str","conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2,","%{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval):",": str, optional Unit system Default is \"mic-pk-pk\" kwargs : optional Additional key","the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference for","/ 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace(","title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout(","angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[],","self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2,","{}\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, )","be between 0 and 100 inclusive. units : str, optional Unit system Default","**kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof","magnitude : array Magnitude of the frequency response for node for each frequency.","/ 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\",","color=colors1[i]), legendgroup=\"perc{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\"","p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof","ST_CampbellResults: \"\"\"Store stochastic results and provide plots for Campbell Diagram. It's possible to","input/output. phase : array Array with the frequencies, phase of the frequency response","subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\"white\", polar_bgcolor=\"white\", width=1800,","opacity=0.5, name=\"confidence interval: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude:","phase angle vs frequency. \"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval,","i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i,","frequencies log_dec : array Array with the Logarithmic decrement Returns ------- subplots :","1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase:","with the force response for each node for each frequency. frequency_range : array","element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs","the phase response given an output and an input using bokeh. Parameters ----------","rotor system in a 2D view. Parameters ---------- node : int Select the","+ \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit:","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\",","height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults:","axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\",","2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2)","\"\"\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional","range in rad/s. wd : array Array with the damped natural frequencies log_dec","default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units,","row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2,",") x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude,","nodes_pos: array Rotor nodes axial positions. number_dof : int Number of degrees of","_probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] *","- p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2,","graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \"\"\" def __init__(self,",": Plotly graph_objects.Figure() The figure object with the plot. \"\"\" conf_interval = np.sort(conf_interval)","------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \"\"\" if","x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency:","fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1,","for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy]))","for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy]))","for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\",","go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time:","legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20),","the plot. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs :","__init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec","legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" +","axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude:","np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p","\"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude =","node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X -","{i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency:","p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile:","be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object","interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\",","time_range : 1-dimensional array Time array. yout : array System response. xout :","np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt:","mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True,","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","+ 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, )","**kwargs): \"\"\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile : list,","), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig def plot(self,","axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x,","conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters ----------","speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\"mic-pk-pk\",","legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p","phase response given an output and an input using bokeh. Parameters ---------- percentile",") for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data,","%{y:.2e}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p,","line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else","response (2D). This function plots orbits for a given node on the rotor","for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude,","hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace(","...) *See Plotly Python Figure Reference for more information. Returns ------- subplots :","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,","enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof + 1","to check other speeds which also excite a specific natural frequency. Two options","2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 =","fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None,",") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p","x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hoverinfo=\"none\", **kwargs,",") fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\"lightgray\",","check other speeds which also excite a specific natural frequency. Two options for","compute, which must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure()","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results","fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", legendgroup=f\"Probe {i +","\"\"\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 =","ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\"Mean\",","name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs,","= dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items():","\"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j,","/ 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 -","p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3","subplots class ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for Frequency Response. Parameters","line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\"","= go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\",","(m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label =",": Plotly graph_objects.Figure() The figure object with the plot. units : str, optional","word arguments can be passed to change the plot layout only (e.g. width=1000,","np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof","%{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j,","interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs)","probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2)","{i + 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced","enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5,","color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"),","from plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import","change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure","* n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n","= np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\"","multiples harmonics in a single plot to check other speeds which also excite","each frequency. number_dof = int Number of degrees of freedom per shaft element's","graph_objects.Figure() The figure object with the plot. \"\"\" ndof = self.number_dof default_values =","set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults:","probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots","module. This module returns graphs for each type of analyses in st_rotor_assembly.py. \"\"\"","given a set of probes. Parameters ---------- dof : int Degree of freedom","plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots","1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])),","thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i +","orbit response. percentile : list, optional Sequence of percentiles to compute, which must","**kwargs): \"\"\"Plot orbit response (2D). This function plots orbits for a given node","fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}%","for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50","conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the unbalance","- Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)),","given an output and an input using bokeh. Parameters ---------- percentile : list,","ndof * n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5,","p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i +","fig class ST_ForcedResponseResults: \"\"\"Store stochastic results and provide plots for Forced Response. Parameters","fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe {i","frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range =","intervals to compute, which must be between 0 and 100 inclusive. args: optional","if fig is None: fig = go.Figure() color_i = 0 color_p = 0","conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v)","plots orbits for a given node on the rotor system in a 2D","%{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for i, p","opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for","opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude:","name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\",","p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, )","+ 1} - confidence interval: {p}%\", ) ) color_i += 1 fig.update_layout( polar=dict(","%{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) )","y=np.mean(self.phase, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs,","theta=np.concatenate((p3, p4[::-1])), thetaunit=\"radians\", line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs,","information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot.","per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object","\"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval =","the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if","where the probe is located. orientation : float, probe orientation angle about the","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\",","**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 +","for each node on the rotor system in a 3D view. Parameters ----------","fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p),","an input using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles","x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i + 1} - confidence","\"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label =","tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\"white\", legend=dict( font=dict(family=\"sans-serif\",","speed range in rad/s. magnitude : array Array with the frequencies, magnitude (dB)","(e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more information. Returns",": optional Additional key word arguments can be passed to change the plot","legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" +","interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time (s)</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\")","- Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(","hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig","p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i],","2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p /","k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots","layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more information.","response using Plotly. Parameters ---------- dof : int Degree of freedom. units :","1), line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs, ) ) for","the plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units","= np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:,","self.yout[..., ndof * n + 1], 50 - p / 2, axis=0 ),","self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p /","line=dict(width=3, color=colors1[j]), legendgroup=\"mean{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Log Dec: %{y:.3f}\"), **kwargs, ) ) for","y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5,","be between 0 and 100 inclusive. args: optional harmonics : list, optional List","in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j,","opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"), **kwargs,","2}], [{}, None]] ) for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data","Two options for plooting are available: Matplotlib and Bokeh. The user chooses between","angle). node : int indicate the node where the probe is located. orientation","color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i + 1} -","the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference","amplitude vs frequency phase angle vs frequency. \"\"\" def __init__(self, speed_range, magnitude, phase):","**kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","*args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1)","+ \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) for j, p in","fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict(","np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter(","%{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase,","= ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2","* np.pi if i < 0 else i for i in aux_phase] )","for frequency and log dec. \"\"\" def __init__(self, speed_range, wd, log_dec): self.speed_range =","__init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase","y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile:","50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p /",") fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p","+ \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in","np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p","fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile,","- Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", ) ) for j, p in enumerate(percentile): fig.add_trace(","p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50","Response. Parameters ---------- force_resp : array Array with the force response for each","np.sort(percentile) if units == \"m\": r_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\":","legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1]))","p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1)","Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout( plot_bgcolor=\"white\", width=1200,","plots the unbalance response magnitude. Parameters ---------- probe : list of tuples List","np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:,","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for","axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" +","def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \"\"\"Plot time response.","p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\",","1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_p += 1 x","the response. percentile : list, optional Sequence of percentiles to compute, which must","): \"\"\"Plot amplitude vs frequency. This method plots the frequency response magnitude given","go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0),","2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p /","k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase,","speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def","fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\",","go from plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme","(e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more information. Returns","ST_FrequencyResponseResults: \"\"\"Store stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range","fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False),","size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \"\"\"Store stochastic results","plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units","the state vector. nodes_list: array list with nodes from a rotor model. nodes_pos:","i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[...,","possible to visualize multiples harmonics in a single plot to check other speeds","output and an input using bokeh. Parameters ---------- percentile : list, optional Sequence","opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" +","graphs for each type of analyses in st_rotor_assembly.py. \"\"\" import numpy as np","p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range,","to visualize multiples harmonics in a single plot to check other speeds which","change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference","n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6,","Plotly graph_objects.Figure() The figure object with the plot. units : str Magnitude unit","linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\",","return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response","axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency:","from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\" # set Plotly palette of colors","+ 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Rotor speed</b>\", title_font=dict(family=\"Arial\", size=20),",": array Array with the frequencies. magnitude : array Magnitude of the frequency","None]] ) # fmt: on for data in fig0[\"data\"]: data.showlegend = False fig.add_trace(data,","+ 1} - Mean\", line=dict(width=3.0), hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","of degrees of freedom per shaft element's node. Returns ------- subplots : Plotly","dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1]","\"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof","= self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if","== \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" for","range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\"Mean - Mode {}\".format(j + 1),","given a tuple of probes with their nodes and orientations. Parameters ---------- probe","bordercolor=\"black\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase","\"<b>Amplitude (dB)</b>\" if fig is None: fig = go.Figure() color_p = 0 color_i","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True,","tuples List with tuples (node, orientation angle). node : int indicate the node","/ 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3,","percentile=[], conf_interval=[], **kwargs): \"\"\"Plot phase angle response. This method plots the phase response","of the frequency response for each pair input/output. Returns ------- subplots : Plotly","%{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","%{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval):","Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","0 refers to +X direction. percentile : list, optional Sequence of percentiles to","thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i + 1} - percentile: {p}%\", legendgroup=f\"Probe {i","axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,","**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof *","1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ),","- percentile: {p}%\", legendgroup=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\",","a 3D view. Parameters ---------- percentile : list, optional Sequence of percentiles to","1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1,","int Degree of freedom. units : str Magnitude unit system. Default is \"mic-pk-pk\"","\"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i, p in","response magnitude given an output and an input using Plotly. Parameters ---------- percentile","plots the time response given a tuple of probes with their nodes and",") ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 =","axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else False, hovertemplate=(","the plot. \"\"\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for","v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof *","the plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None:",": array Phase of the frequency response for node for each frequency. number_dof","size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20),","np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[...,","1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\", )","self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe,","for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2,","element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object with the","j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs,","go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}{}\".format(j, i), hovertemplate=(","angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\"Probe","2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i","= px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results and provide plots","**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 +","p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0)","\"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig","= \"<b>Amplitude (dB)</b>\" if fig is None: fig = go.Figure() color_p = 0","more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with the","np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0 color_p =","conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the frequency","results and provide plots for Campbell Diagram. It's possible to visualize multiples harmonics","p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\"Probe {i + 1} - percentile: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\"","k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1]))","node to display the respective orbit response. percentile : list, optional Sequence of","frequency response for node for each frequency. number_dof = int Number of degrees","The figure object with the plot. \"\"\" if fig is None: fig =","percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is","showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i","plots the frequency and phase response given an output and an input. Parameters","= go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe):","Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\"","orientation angle). node : int indicate the node where the probe is located.","graph_objects.Figure() The figure object with the plot. kwargs : optional Additional key word","name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\", hovertemplate=(","percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots the frequency and","optional Sequence of percentiles to compute, which must be between 0 and 100","= np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0 color_i","= probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\"radians\", mode=\"lines\",","2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 =","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(","phase response given a set of probes. Parameters ---------- probe : list of","orbits for a given node on the rotor system in a 2D view.","aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 *","an input. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","Number of degrees of freedom per shaft element's node. Returns ------- subplots :","Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \"\"\" fig0","plot. units : str, optional Unit system Default is \"mic-pk-pk\". kwargs : optional","1} - confidence interval: {p}%\", legendgroup=f\"Probe {i + 1} - confidence interval: {p}%\",","inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. args :","fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0),","as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \"browser\"","2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range,","tickfont=dict(size=14), gridcolor=\"lightgray\", linecolor=\"black\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[],","interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \"Nodal Position:","legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1]))","- Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) for","fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for","polar forced response using Plotly. Parameters ---------- dof : int Degree of freedom.","X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store","in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1,","to observe the response. percentile : list, optional Sequence of percentiles to compute,",": Plotly graph_objects.Figure() The figure object with the plot. args : optional Additional","+ \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p","= dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval,","v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0]","layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more","float, probe orientation angle about the shaft. The 0 refers to +X direction.","), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\") fig.update_yaxes(title_text=\"<b>Amplitude</b>\") fig.update_layout(title=\"<b>Rotor Orbit: node {}</b>\".format(node)), return fig def","50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p /","compute, which must be between 0 and 100 inclusive. units : str, optional","array Time evolution of the state vector. nodes_list: array list with nodes from","direction. percentile : list, optional Sequence of percentiles to compute, which must be","- p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]),","plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This","line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else False, hovertemplate=( \"Nodal","Additional plot axes kwargs : optional Additional key word arguments can be passed",": array System response. xout : array Time evolution of the state vector.","showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False), zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig","Response. Parameters ---------- time_range : 1-dimensional array Time array. yout : array System","pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is None: fig = go.Figure()","confidence intervals to compute, which must be between 0 and 100 inclusive. units","graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" fig0","list, optional List withe the harmonics to be plotted. The default is to","frequency and log dec. \"\"\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values =","subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14),","Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hovertemplate=( \"Frequency: %{x:.3f}<br>\" + \"Frequency: %{y:.3f}\"","Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ),","optional Additional key word arguments can be passed to change the plot layout","j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0)","y_axis_label = \"<b>Amplitude (dB)</b>\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is","angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle),","r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig is","in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 =","= np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 -","them using the attribute plot_type. The default is bokeh Parameters ---------- speed_range :","cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in fig1[\"data\"]: subplots.add_trace(data,",": 1-dimensional array Time array. yout : array System response. xout : array","np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2, axis=0) fig.add_trace(","= np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2, axis=0)","+ p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2,","node {}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit","p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\",","to display the respective orbit response. percentile : list, optional Sequence of percentiles","the shaft. The 0 refers to +X direction. percentile : list, optional Sequence","self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2,","1, 1) for data in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis,","nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list","= self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items():","in fig1[\"data\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data,","kwargs : optional Additional key word arguments can be passed to change the","v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\"radians\", line=dict(width=3.0,","if j == 0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X -","row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict(","y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] *",": list, optional Sequence of percentiles to compute, which must be between 0","fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_layout(",") ) fig.update_xaxes( title_text=\"<b>Frequency</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, )","(2D). This function plots orbits for a given node on the rotor system","plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for","px from plotly import graph_objects as go from plotly import io as pio","opacity=0.5, name=f\"Probe {i + 1} - confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude:","fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\",","fig.update_yaxes(title_text=\"<b>Amplitude</b>\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit","ndof * node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof","for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs","is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[...,","np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[...,","array Array with the damped natural frequencies log_dec : array Array with the","Array with the speed range in rad/s. magnitude : array Array with the","= np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @","The default is bokeh Parameters ---------- speed_range : array Array with the speed","Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout","information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency","else i for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i]","a given node on the rotor system in a 2D view. Parameters ----------","linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Phase Angle</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","to plot 1x. kwargs : optional Additional key word arguments can be passed","- confidence interval: {p}%\", hovertemplate=(\"Time: %{x:.3f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Time","np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\",","n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\"percentile:","given an output and an input. Parameters ---------- percentile : list, optional Sequence","array Array with the frequencies. magnitude : array Magnitude of the frequency response","= make_subplots(rows=1, cols=2) for data in fig0[\"data\"]: subplots.add_trace(data, 1, 1) for data in","Parameters ---------- speed_range : array Array with the speed range in rad/s. magnitude","is \"mic-pk-pk\" polar_kwargs : optional Additional key word arguments can be passed to","fig = go.Figure() color_i = 0 color_p = 0 for i, p in","thetaunit=\"radians\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i], opacity=0.5, name=f\"Probe {i + 1} - confidence interval:","excite a specific natural frequency. Two options for plooting are available: Matplotlib and","0% and 100% inclusive. units : str, optional Unit system Default is \"mic-pk-pk\".","and phase response given an output and an input. Parameters ---------- percentile :","fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in","%{y:.3f}\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1),","other speeds which also excite a specific natural frequency. Two options for plooting","x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\"lines\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\",","Parameters ---------- probe : list of tuples List with tuples (node, orientation angle).","view. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which","thetaunit=\"radians\", line=dict(width=3.0, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency:","%{y:.2f}\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p","v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"),","be passed to change the plot layout only (e.g. width=1000, height=800, ...). *See","fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} - Mean\", line=dict(width=3.0),","np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\"lines\",","%{x:.3f}<br>\" + \"Frequency: %{y:.3f}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):","in fig1[\"data\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1,","gridcolor=\"lightgray\", showline=True, linewidth=2.5, linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Logarithmic decrement</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\",","enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\"lines\", line=dict(width=2.5, color=colors1[color_p]), name=f\"Probe {i","1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\"","tuples (node, orientation angle). node : int indicate the node where the probe","{}</b>\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \"\"\"Plot orbit response","np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y","x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1],","angle vs frequency. \"\"\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp =","legendgroup=\"percentile{}\".format(i), hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, )","Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i,","**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof *","(_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0])","p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2","), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50","axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase,","%{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y - Amplitude: %{z:.2e}\" ), **kwargs,","fig=None, **kwargs): \"\"\"Plot frequency response. This method plots the phase response given a","* node + 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[...,","np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p =","legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def plot(self, percentile=[],","= False fig.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis,","\"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in","color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig","plot. kwargs : optional Additional key word arguments can be passed to change","plot. \"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units ==","**kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\"Arial\", size=14), gridcolor=\"lightgray\", exponentformat=\"power\", ), angularaxis=dict(","1} - Mean\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ),","row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2,","axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe {i +","conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method plots the frequency and phase","**kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0,","color=\"black\", dash=\"dashdot\"), showlegend=False, mode=\"lines\", ) ) for j, n in enumerate(self.nodes_list): x =","response. This method plots the frequency and phase response given a set of",") return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \"\"\"Plot Campbell Diagram. This","1], axis=0), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y","Dec: %{y:.3f}\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,","percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe,","phase : array Array with the frequencies, phase of the frequency response for","* self.number_dof + 1 angle = p[1] # fmt: off operator = np.array(","legendgroup=\"conf_interval{}\".format(p), showlegend=False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" + \"Y","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" if fig","fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\",","\"\"\" if units == \"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\":","to compute, which must be between 0 and 100 inclusive. conf_interval : list,","row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\",","dof : int Degree of freedom. units : str Magnitude unit system. Default",") angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase,","graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \"\"\" fig0 =","phase of the frequency response for each pair input/output. Returns ------- subplots :","Plotly graph_objects.Figure() The figure object with the plot. \"\"\" ndof = self.number_dof default_values","of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \"\"\"Store stochastic results","for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle","for Campbell Diagram. It's possible to visualize multiples harmonics in a single plot","width=1200, height=900, legend=dict( font=dict(family=\"sans-serif\", size=14), bgcolor=\"white\", bordercolor=\"black\", borderwidth=2, ), ) return fig def","Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(","* self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof *","color=list(tableau_colors)[i]), name=f\"Probe {i + 1} - Mean\", legendgroup=f\"Probe {i + 1} - Mean\",","hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for","- Mode {}\".format(p, j + 1), legendgroup=\"conf{}{}\".format(j, i), hoverinfo=\"none\", **kwargs, ) ) fig.update_xaxes(","response given an output and an input using bokeh. Parameters ---------- percentile :","---------- probe : list of tuples List with tuples (node, orientation angle). node","harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional","), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\"<b>Rotor Length</b>\"), showspikes=False), yaxis=dict(title=dict(text=\"<b>Amplitude - X</b>\"), showspikes=False),","+ \"<b>Frequency: %{customdata:.2f}</b>\" ), ) ) color_p += 1 for j, p in","of percentiles to compute, which must be between 0 and 100 inclusive. conf_interval","specific natural frequency. Two options for plooting are available: Matplotlib and Bokeh. The","and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout :","This method plots the phase response given a set of probes. Parameters ----------","col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\", width=1800,","), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50","percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot polar forced response using Plotly. Parameters","axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p / 2,","x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range","%{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def","fig : Plotly graph_objects.Figure() The figure object with the plot. \"\"\" ndof =","fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp","fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\"Probe {i + 1} -","n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n +","dofy = p[0] * self.number_dof + 1 angle = p[1] # fmt: off","to compute, which must be between 0 and 100 inclusive. harmonics: list, optional","is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\"type\": \"polar\", \"rowspan\": 2}], [{},","p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])),","conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v)","\"browser\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24","{}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) ) x = np.concatenate((self.speed_range,","color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), **kwargs, ) ) fig.update_layout( polar=dict(","of the frequency response for node for each frequency. number_dof = int Number","p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1)","\"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label","frequency. \"\"\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude","== \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values","row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\"white\",","Time evolution of the state vector. nodes_list: array list with nodes from a","/ 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\"toself\", fillcolor=colors1[j], opacity=0.5, name=f\"Probe","/ 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3","= p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle =","to change the plot (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure","line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), legendgroup=\"percentile{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, ) )","list of tuples List with tuples (node, orientation angle). node : int indicate","+ 1} - confidence interval: {p}%\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) color_i +=","plot. \"\"\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \"m\": r_axis_label","+ \"Phase: %{y:.2f}\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p","This method plots the unbalance response magnitude. Parameters ---------- probe : list of","for data in fig0[\"data\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\"data\"]: subplots.add_trace(data, row=2,","\"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\" + \"<b>Frequency: %{customdata:.2f}</b>\" ), **kwargs, ) ) for","self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k,","**kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{},","%{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50","polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\"E\"), angularaxis=dict(exponentformat=\"E\"), ), **kwargs, ) return fig def plot( self, probe,","borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the","in fig1[\"data\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\"data\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis,","frequency. phase : array Phase of the frequency response for node for each","line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns","in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 =","opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Phase: %{y:.2f}\"), **kwargs, ) )","fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency","= np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),","stochastic results and provide plots for Forced Response. Parameters ---------- force_resp : array","np from plotly import express as px from plotly import graph_objects as go","axis=1), opacity=1.0, name=\"Mean\", line=dict(width=3, color=\"black\"), legendgroup=\"mean\", hovertemplate=(\"Frequency: %{x:.2f}<br>\" + \"Amplitude: %{y:.2e}\"), **kwargs, )","for a given node on the rotor system in a 2D view. Parameters","- p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\",","Response. Parameters ---------- speed_range : array Array with the speed range in rad/s.","np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig =","%{x:.2f}<br>Phase: %{y:.2f}\", ) ) color_i += 1 fig.update_xaxes(title_text=\"<b>Frequency</b>\") fig.update_yaxes(title_text=\"<b>Phase Angle</b>\") fig.update_layout(**kwargs), return fig","plots for Campbell Diagram. It's possible to visualize multiples harmonics in a single","1} - Mean\", hovertemplate=\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\", ) ) for j, p in enumerate(percentile):","\"mic-pk-pk\". kwargs : optional Additional key word arguments can be passed to change","2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in","system Default is \"mic-pk-pk\". kwargs : optional Additional key word arguments can be","a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number","else: y_axis_label = \"<b>Amplitude (dB)</b>\" default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile =","- Amplitude: %{x:.2e}<br>\" + \"Y - Amplitude: %{y:.2e}\" ), **kwargs, ) ) fig.update_xaxes(title_text=\"<b>Amplitude</b>\")","= np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0,","damped natural frequencies log_dec : array Array with the Logarithmic decrement Returns -------","p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\"toself\", fillcolor=colors1[i], opacity=0.5, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf{}\".format(i), hovertemplate=(\"Frequency: %{x:.2f}<br>\" +","{p}%\", legendgroup=f\"Probe {i + 1} - percentile{p}\", hovertemplate=( \"<b>Amplitude: %{r:.2e}</b><br>\" + \"<b>Phase: %{theta:.2f}</b><br>\"","\"m\": y_axis_label = \"<b>Amplitude (m)</b>\" elif units == \"mic-pk-pk\": y_axis_label = \"<b>Amplitude (μ","**kwargs): \"\"\"Plot frequency response. This method plots the frequency and phase response given","and provide plots for Frequency Response. Parameters ---------- speed_range : array Array with","- p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]),","* np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for","0 else False, hovertemplate=( \"Nodal Position: %{x:.2f}<br>\" + \"X - Amplitude: %{y:.2e}<br>\" +","conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v)","Amplitude: %{y:.2e}\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =","optional Sequence of confidence intervals to compute, which must be between 0 and","\"\"\"Plot phase angle response. This method plots the phase response given an output","return fig def plot(self, percentile=[], conf_interval=[], units=\"mic-pk-pk\", **kwargs): \"\"\"Plot frequency response. This method","the frequency response magnitude given an output and an input using Plotly. Parameters","/ 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\"lines\", line=dict(width=1, color=colors2[color_i]), fill=\"toself\", fillcolor=colors2[color_i],","Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure","freedom to observe the response. percentile : list, optional Sequence of percentiles to","linecolor=\"black\", mirror=True, ) fig.update_yaxes( title_text=\"<b>Damped Natural Frequencies</b>\", title_font=dict(family=\"Arial\", size=20), tickfont=dict(size=16), gridcolor=\"lightgray\", showline=True, linewidth=2.5,","y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof","axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\"percentile: {}%\".format(p), hovertemplate=( \"X - Amplitude: %{x:.2e}<br>\" + \"Y","a set of probes. Parameters ---------- probe : list of tuples List with","each pair input/output. phase : array Array with the frequencies, phase of the","\"mic-pk-pk\": r_axis_label = \"<b>Amplitude (μ pk-pk)</b>\" else: r_axis_label = \"<b>Amplitude (dB)</b>\" if fig","3D view. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,","method plots the phase response given a set of probes. Parameters ---------- probe","return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \"\"\"Plot the log_dec vs frequency.","*args, **kwargs): \"\"\"Plot orbit response (3D). This function plots orbits for each node","method plots the phase response given an output and an input using bokeh.","\"\"\"Plot frequency response. This method plots the phase response given a set of","self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\"Mean -","for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with","the node where the probe is located. orientation : float, probe orientation angle","p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1)","default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in","zaxis=dict(title=dict(text=\"<b>Amplitude - Y</b>\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \"\"\"Store stochastic results","* h, opacity=1.0, name=\"{}x speed\".format(h), line=dict(width=3, color=colors1[j], dash=\"dashdot\"), legendgroup=\"speed{}\".format(j), hovertemplate=(\"Frequency: %{x:.3f}<br>\" + \"Frequency:","graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \"\"\" def","node for each frequency. number_dof = int Number of degrees of freedom per","response. xout : array Time evolution of the state vector. nodes_list: array list","+ p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\"confidence interval: {}%\".format(p), legendgroup=\"conf_interval{}\".format(p),","+ \"Y - Amplitude: %{z:.2e}\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,","fig=None, units=\"mic-pk-pk\", **kwargs, ): \"\"\"Plot frequency response. This method plots the unbalance response","self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p /","freedom. units : str Magnitude unit system. Default is \"mic-pk-pk\" polar_kwargs : optional","decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency","1], axis=0), line=dict(width=5, color=\"black\"), name=\"Mean\", legendgroup=\"mean\", showlegend=True if j == 0 else False,","with the plot. \"\"\" if fig is None: fig = go.Figure() default_values =",") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1),",") color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval):","x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\"toself\", fillcolor=colors1[j], opacity=0.3, name=\"confidence interval: {}% - Mode"],"string":"[\n \"fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in\",\n \"is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile =\",\n \"p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50\",\n \"np.pi if i < 0 else i for i in aux_phase] ) angle\",\n \"axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\"\",\n \"rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number of\",\n \"row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\",\",\n \"the rotor system in a 2D view. Parameters ---------- node : int Select\",\n \"= np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +\",\n \"plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the\",\n \"kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]):\",\n \"results and provide plots for Time Response and Orbit Response. Parameters ---------- time_range\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude,\",\n \"{i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude:\",\n \"in rad/s. wd : array Array with the damped natural frequencies log_dec :\",\n \"The figure object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval)\",\n \"rad/s. wd : array Array with the damped natural frequencies log_dec : array\",\n \"legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p\",\n \"name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=(\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict(\",\n \"each frequency. phase : array Phase of the frequency response for node for\",\n \"natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"between 0% and 100% inclusive. units : str, optional Unit system Default is\",\n \"None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]:\",\n \"optional Additional plot axes kwargs : optional Additional key word arguments can be\",\n \"number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency\",\n \"p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1}\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\",\",\n \"(dB) of the frequency response for each pair input/output. phase : array Array\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure()\",\n \"kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3,\",\n \"go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n +\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval =\",\n \"= wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the\",\n \"+ p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2,\",\n \"elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude\",\n \"+ 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, )\",\n \"r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig =\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i\",\n \"axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe\",\n \"= 0 color_p = 0 for i, p in enumerate(probe): dofx = p[0]\",\n \"- p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50\",\n \"compute, which must be between 0 and 100 inclusive. args: optional harmonics :\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval,\",\n \"/ 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace(\",\n \"in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 =\",\n \"r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\"\",\n \"), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\",\",\n \"plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters\",\n \"colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide\",\n \"col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1)\",\n \"np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0 color_i =\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[],\",\n \"int indicate the node where the probe is located. orientation : float, probe\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 =\",\n \") fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), )\",\n \"2}], [{}, None]] ) # fmt: on for data in fig0[\\\"data\\\"]: data.showlegend =\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[],\",\n \"plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This\",\n \"This method plots the time response given a tuple of probes with their\",\n \"The figure object with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\")\",\n \"for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1)\",\n \"\\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) )\",\n \"return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This\",\n \"Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional Additional key\",\n \"aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range,\",\n \"hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p in\",\n \"probe is located. orientation : float, probe orientation angle about the shaft. The\",\n \"fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}],\",\n \"col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1,\",\n \"1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval:\",\n \"interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, )\",\n \"also excite a specific natural frequency. Two options for plooting are available: Matplotlib\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[],\",\n \"i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1)\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof\",\n \"{}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs,\",\n \"from plotly import graph_objects as go from plotly import io as pio from\",\n \"1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\"\",\n \"1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5,\",\n \"The figure object with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof,\",\n \"figure object with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval\",\n \"= px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for Campbell Diagram.\",\n \"%{y:.2e}\\\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp,\",\n \"with the speed range in rad/s. wd : array Array with the damped\",\n \"angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class\",\n \"and an input. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\"\",\n \"go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j,\",\n \"with the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif\",\n \"2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal\",\n \"go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval:\",\n \"* n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\",\",\n \"Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes\",\n \"response using Plotly. Parameters ---------- probe : list of tuples List with tuples\",\n \"np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] *\",\n \"fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0),\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label\",\n \"available: Matplotlib and Bokeh. The user chooses between them using the attribute plot_type.\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional\",\n \"mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p /\",\n \"axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for\",\n \"with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with\",\n \"col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data,\",\n \"Bokeh. The user chooses between them using the attribute plot_type. The default is\",\n \"\\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units ==\",\n \"%{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[...,\",\n \"nodes from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof :\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"\\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v\",\n \"50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p /\",\n \"of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly import express\",\n \"Degree of freedom. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs\",\n \"operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2\",\n \"legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, )\",\n \"forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof\",\n \"1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x\",\n \"for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2,\",\n \"axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs,\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p),\",\n \"line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j +\",\n \"degrees of freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure()\",\n \"freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \") # fmt: on for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1,\",\n \"fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend =\",\n \"\\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j],\",\n \"n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0\",\n \"Number of degrees of freedom per shaft element's node Returns ------- fig :\",\n \"mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p\",\n \"for node for each frequency. number_dof = int Number of degrees of freedom\",\n \"set of probes. Parameters ---------- dof : int Degree of freedom to observe\",\n \"50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p /\",\n \"** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe\",\n \"legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", ) ) color_i += 1\",\n \"line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase:\",\n \"provide plots for Forced Response. Parameters ---------- force_resp : array Array with the\",\n \"col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), )\",\n \"%{y:.2f}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p,\",\n \"Python Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly\",\n \"showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile:\",\n \"class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for Campbell Diagram. It's possible\",\n \"False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2,\",\n \"name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]),\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],\",\n \"\\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase\",\n \"name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile:\",\n \"fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i +\",\n \"1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval:\",\n \"xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), )\",\n \"fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots orbits for each\",\n \"%{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list): x\",\n \"amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase,\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6,\",\n \"fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 =\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p,\",\n \"- Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), )\",\n \"= \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in\",\n \"line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time:\",\n \"in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2,\",\n \"+ 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\",\",\n \"x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50\",\n \"1x. kwargs : optional Additional key word arguments can be passed to change\",\n \"axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3,\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" +\",\n \"- Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase:\",\n \"x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n\",\n \"axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1}\",\n \"Array with the frequencies. magnitude : array Magnitude of the frequency response for\",\n \"percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots the\",\n \"= np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @\",\n \"- p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\",\",\n \"frequency_range : array Array with the frequencies. magnitude : array Magnitude of the\",\n \"+ np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude):\",\n \"node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots\",\n \"information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot.\",\n \"subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900,\",\n \"borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ):\",\n \"2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence\",\n \"fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y -\",\n \"p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50\",\n \"speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\",\",\n \"enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase,\",\n \"go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude:\",\n \"This method plots the frequency response magnitude given an output and an input\",\n \"enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof *\",\n \"color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X\",\n \"figure with diagrams for frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval,\",\n \"response. This method plots the time response given a tuple of probes with\",\n \"fig is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile\",\n \"fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This method plots the time response\",\n \"== 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\"\",\n \"j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5,\",\n \"height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), )\",\n \"Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d(\",\n \"row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for\",\n \"/ 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5,\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" +\",\n \"aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp,\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3,\",\n \"node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\"\",\n \"legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p in\",\n \"int Degree of freedom to observe the response. percentile : list, optional Sequence\",\n \"np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag\",\n \"axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\"\",\n \"np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:,\",\n \"\\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for j,\",\n \"on for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data\",\n \") ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, )\",\n \"plots for Forced Response. Parameters ---------- force_resp : array Array with the force\",\n \"\\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency:\",\n \"for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1)\",\n \"which must be between 0 and 100 inclusive. args: optional harmonics : list,\",\n \"p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i),\",\n \"r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig = go.Figure() color_p =\",\n \"1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node\",\n \"= dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label\",\n \"with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval,\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and\",\n \"= nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[],\",\n \"Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot\",\n \"* self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i <\",\n \"Plotly Python Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots()\",\n \"Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor\",\n \"opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=(\",\n \"+ \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for\",\n \"Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" def\",\n \"+X direction. percentile : list, optional Sequence of percentiles to compute, which must\",\n \"fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof *\",\n \"dof : int Degree of freedom to observe the response. percentile : list,\",\n \"{}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1),\",\n \"line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i,\",\n \"= make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for\",\n \"Phase of the frequency response for node for each frequency. number_dof = int\",\n \"legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) )\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return\",\n \"between 0 and 100 inclusive. kwargs : optional Additional key word arguments can\",\n \"cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on for\",\n \"subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec.\",\n \"axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x,\",\n \"the frequency response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots()\",\n \"- Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and\",\n \") angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0),\",\n \"\\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar(\",\n \"dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs)\",\n \"for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with\",\n \"response. This method plots the phase response given a set of probes. Parameters\",\n \"compute, which must be between 0 and 100 inclusive. conf_interval : list, optional\",\n \"opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure\",\n \"hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i, p\",\n \"+ 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in\",\n \"speed_range : array Array with the speed range in rad/s. magnitude : array\",\n \"polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return\",\n \"dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args,\",\n \") fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args,\",\n \"between 0 and 100 inclusive. harmonics: list, optional List withe the harmonics to\",\n \"), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for\",\n \"percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the\",\n \"%{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"= np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3,\",\n \"fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in\",\n \"1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in enumerate(percentile):\",\n \"+ 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1\",\n \"fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout(\",\n \"subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis,\",\n \"mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i +\",\n \"type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly import\",\n \"and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd\",\n \"), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0),\",\n \"phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", )\",\n \"name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency:\",\n \"+ 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3,\",\n \"line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ),\",\n \"x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\"\",\n \"2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:,\",\n \"kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in\",\n \"- Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace(\",\n \"probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) **\",\n \"Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments can be passed\",\n \"fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs):\",\n \"line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) )\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 +\",\n \"Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) )\",\n \"), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for\",\n \"must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14),\",\n \"r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v\",\n \"units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response\",\n \"given a set of probes. Parameters ---------- probe : list of tuples List\",\n \"* n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j ==\",\n \"using the attribute plot_type. The default is bokeh Parameters ---------- speed_range : array\",\n \"class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Time Response and Orbit\",\n \"**kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units,\",\n \"plots the frequency response magnitude given an output and an input using Plotly.\",\n \"go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude:\",\n \"{\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1,\",\n \"width=1000, height=800, ...). *See Plotly Python Figure Reference for more information. Returns -------\",\n \"), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0\",\n \"per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with\",\n \"name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency:\",\n \"# fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe\",\n \"customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" +\",\n \"percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ----------\",\n \"p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0)\",\n \") fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout(\",\n \"This module returns graphs for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import\",\n \"each node on the rotor system in a 3D view. Parameters ---------- percentile\",\n \"Additional key word arguments can be passed to change the plot layout only\",\n \"height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def\",\n \"* node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6,\",\n \"data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis,\",\n \"else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig = go.Figure() color_p\",\n \"+ 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\"\",\n \"figure object with the plot. \\\"\\\"\\\" if fig is None: fig = go.Figure()\",\n \"log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" +\",\n \"time response. This method plots the time response given a tuple of probes\",\n \"in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2,\",\n \"= go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,\",\n \"phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[],\",\n \"enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] *\",\n \"speed range in rad/s. wd : array Array with the damped natural frequencies\",\n \"word arguments can be passed to change the plot layout (e.g. width=800, height=600,\",\n \"np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\",\",\n \"must be between 0 and 100 inclusive. args: optional harmonics : list, optional\",\n \"p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude:\",\n \"= 0 color_i = 0 for i, p in enumerate(probe): dofx = p[0]\",\n \"Array with the damped natural frequencies log_dec : array Array with the Logarithmic\",\n \"0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy =\",\n \":, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx],\",\n \"- Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *\",\n \"+ (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :,\",\n \"object with the plot. units : str, optional Unit system Default is \\\"mic-pk-pk\\\".\",\n \"- percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" +\",\n \"p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"frequency and phase response given a set of probes. Parameters ---------- dof :\",\n \"vs frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs)\",\n \"p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile\",\n \"fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\",\",\n \"object with the plot. args : optional Additional plot axes kwargs : optional\",\n \"n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n +\",\n \"+ p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2,\",\n \"- p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1,\",\n \"** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace(\",\n \"interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", ) ) color_i\",\n \"= False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data,\",\n \"y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, )\",\n \"vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs)\",\n \"respective orbit response. percentile : list, optional Sequence of percentiles to compute, which\",\n \"Plotly graph_objects.Figure() The figure object with the plot. units : str, optional Unit\",\n \"opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i\",\n \"2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter(\",\n \"y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile:\",\n \"50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1,\",\n \"v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots\",\n \"angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None,\",\n \"line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} -\",\n \"p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 +\",\n \"= yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof =\",\n \"%{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[...,\",\n \"def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ----------\",\n \"p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])),\",\n \"(μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval)\",\n \"default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\",\",\n \"self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural\",\n \"input using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1}\",\n \"node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"),\",\n \"from plotly import express as px from plotly import graph_objects as go from\",\n \"np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p\",\n \"axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\",\n \"**kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]]\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2,\",\n \"axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence\",\n \"int Number of degrees of freedom per shaft element's node. Returns ------- subplots\",\n \"= np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 -\",\n \"p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5,\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self,\",\n \"speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural\",\n \"name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) )\",\n \"return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced\",\n \"each frequency. frequency_range : array Array with the frequencies. magnitude : array Magnitude\",\n \"1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot(\",\n \"self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot\",\n \"y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency:\",\n \"j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j\",\n \"hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1]\",\n \"y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p),\",\n \"response for node for each frequency. number_dof = int Number of degrees of\",\n \"figure object with the plot. args : optional Additional plot axes kwargs :\",\n \"frequencies, phase of the frequency response for each pair input/output. Returns ------- subplots\",\n \"Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure\",\n \"off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp =\",\n \"fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\"\",\n \"Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide\",\n \"confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase\",\n \"frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1),\",\n \"kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy\",\n \"line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"frequency. This method plots the frequency response magnitude given an output and an\",\n \"user chooses between them using the attribute plot_type. The default is bokeh Parameters\",\n \"go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\"\",\n \"x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"),\",\n \"+ p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2,\",\n \"ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X -\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0,\",\n \"\\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile,\",\n \"units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label =\",\n \"fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots\",\n \"**kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\"\",\n \"subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis,\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for\",\n \"Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence\",\n \"ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Time Response and Orbit Response.\",\n \"%{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for j, p in enumerate(conf_interval): p1\",\n \"rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on\",\n \"List withe the harmonics to be plotted. The default is to plot 1x.\",\n \"frequency and phase response given an output and an input. Parameters ---------- percentile\",\n \"1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase,\",\n \"0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the\",\n \"speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) )\",\n \"+= 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p\",\n \"50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1],\",\n \"fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots =\",\n \"showlegend=False, mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) *\",\n \"the plot. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs :\",\n \"line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude:\",\n \"units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the unbalance response magnitude.\",\n \"interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\"\",\n \"plots the phase response given an output and an input using bokeh. Parameters\",\n \"\\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments can be passed to change\",\n \"{}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal\",\n \"**kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self,\",\n \"go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} -\",\n \"more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude\",\n \"color_i = 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof\",\n \"---------- dof : int Degree of freedom to observe the response. percentile :\",\n \"n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if\",\n \"\\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list,\",\n \"tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24\",\n \"[np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in\",\n \"must be between 0 and 100 inclusive. kwargs : optional Additional key word\",\n \"kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs)\",\n \") for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p /\",\n \"[[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for\",\n \"orbit response (3D). This function plots orbits for each node on the rotor\",\n \"Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"),\",\n \"The figure object with the plot. units : str Magnitude unit system. Default\",\n \"plotting module. This module returns graphs for each type of analyses in st_rotor_assembly.py.\",\n \"intervals to compute, which must be between 0% and 100% inclusive. fig :\",\n \"np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) )\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]),\",\n \"50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p /\",\n \"response. This method plots the unbalance response magnitude. Parameters ---------- probe : list\",\n \"= speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[],\",\n \"axial positions. number_dof : int Number of degrees of freedom per shaft element's\",\n \"Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with\",\n \"magnitude (dB) of the frequency response for each pair input/output. phase : array\",\n \"pio.renderers.default = \\\"browser\\\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2\",\n \"def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout,\",\n \"2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2,\",\n \"%{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i,\",\n \"enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0)\",\n \"kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3,\",\n \"percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units\",\n \") ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p /\",\n \"go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False,\",\n \"{}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" +\",\n \"if fig is None: fig = go.Figure() color_p = 0 color_i = 0\",\n \": str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key\",\n \"- p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1],\",\n \"title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\",\",\n \"%{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j],\",\n \"Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array.\",\n \"mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ),\",\n \"j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i]\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" ndof = self.number_dof\",\n \"**kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the phase response given a set\",\n \"angle response. This method plots the phase response given an output and an\",\n \"j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array(\",\n \"import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette of colors colors1 =\",\n \"(dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar(\",\n \") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0),\",\n \"{i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p +=\",\n \"fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1)\",\n \"default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range,\",\n \"plot axes with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude\",\n \"= phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs\",\n \"optional List withe the harmonics to be plotted. The default is to plot\",\n \"Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments\",\n \"%{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):\",\n \"p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50\",\n \"number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range\",\n \"units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- dof :\",\n \"p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]]\",\n \"), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response.\",\n \"* np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on\",\n \"percentile : list, optional Sequence of percentiles to compute, which must be between\",\n \"1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)],\",\n \"of the frequency response for each pair input/output. phase : array Array with\",\n \"units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional\",\n \"= p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0,\",\n \"the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\":\",\n \"name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} -\",\n \"\\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout\",\n \"i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j],\",\n \"fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters\",\n \"\\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in\",\n \"node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5,\",\n \"theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\",\",\n \"{}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) )\",\n \"log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[],\",\n \"p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments\",\n \"1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j ==\",\n \"in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = (\",\n \"def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function\",\n \"color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j\",\n \"100 inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs :\",\n \"fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter(\",\n \": int Degree of freedom. units : str Magnitude unit system. Default is\",\n \"# fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1}\",\n \"{p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return\",\n \"scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ),\",\n \"list, optional List with the harmonics to be plotted. The default is to\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3,\",\n \"---------- time_range : 1-dimensional array Time array. yout : array System response. xout\",\n \"theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} -\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"diagrams for frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs)\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p\",\n \"* node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X\",\n \"the frequency response for node for each frequency. number_dof = int Number of\",\n \"= np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0 color_p\",\n \"(e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information.\",\n \"fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe\",\n \"color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[],\",\n \"y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X\",\n \"%{y:.3f}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j],\",\n \"width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class\",\n \"name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x =\",\n \"with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif\",\n \"fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i +\",\n \"name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in enumerate(conf_interval):\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1),\",\n \"Sequence of confidence intervals to compute, which must be between 0 and 100\",\n \"\\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile =\",\n \"Plotly. Parameters ---------- probe : list of tuples List with tuples (node, orientation\",\n \"go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i),\",\n \"the plot. args : optional Additional plot axes kwargs : optional Additional key\",\n \") ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[],\",\n \"**kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p\",\n \"str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key word\",\n \"axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0)\",\n \"1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) )\",\n \"{i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p\",\n \"%{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter(\",\n \"import numpy as np from plotly import express as px from plotly import\",\n \"Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p\",\n \"the force response for each node for each frequency. frequency_range : array Array\",\n \"an output and an input using bokeh. Parameters ---------- percentile : list, optional\",\n \"y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\"\",\n \"title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\",\",\n \"display the respective orbit response. percentile : list, optional Sequence of percentiles to\",\n \": array Array with the speed range in rad/s. wd : array Array\",\n \"line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout(\",\n \"phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range\",\n \"can be passed to change the plot layout only (e.g. width=1000, height=800, ...).\",\n \"= np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 +\",\n \"optional Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments\",\n \"z=np.percentile( self.yout[..., ndof * n + 1], 50 - p / 2, axis=0\",\n \"), ) return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response.\",\n \"p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2\",\n \"enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp,\",\n \") fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[],\",\n \"phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i\",\n \"v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics):\",\n \"frequency response for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly\",\n \"fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot\",\n \"fig is None: fig = go.Figure() color_i = 0 color_p = 0 for\",\n \"p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\"\",\n \"+ 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof *\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\",\",\n \"x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50\",\n \") ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None,\",\n \"- Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec:\",\n \"\\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p in enumerate(percentile):\",\n \"magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot\",\n \"= np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 -\",\n \": list of tuples List with tuples (node, orientation angle). node : int\",\n \"+ 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\"\",\n \"Additional key word arguments can be passed to change the plot layout (e.g.\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store\",\n \"customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase:\",\n \"= \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else:\",\n \"= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None:\",\n \"frequency response for node for each frequency. phase : array Phase of the\",\n \") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase,\",\n \"Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout : array\",\n \"fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency\",\n \"hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude:\",\n \"1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /\",\n \"inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional\",\n \"fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args,\",\n \"go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v\",\n \"figure object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile\",\n \"thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\"\",\n \"self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[],\",\n \"for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2,\",\n \"\\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) )\",\n \"linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs):\",\n \"dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in\",\n \"positions. number_dof : int Number of degrees of freedom per shaft element's node\",\n \"with tuples (node, orientation angle). node : int indicate the node where the\",\n \"input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency\",\n \"0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the\",\n \"plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response\",\n \"model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number of degrees\",\n \"view. Parameters ---------- node : int Select the node to display the respective\",\n \"plotly import express as px from plotly import graph_objects as go from plotly\",\n \"for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 +\",\n \"1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude:\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p,\",\n \"stochastic results and provide plots for Time Response and Orbit Response. Parameters ----------\",\n \"= np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 -\",\n \"- p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p),\",\n \"passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python\",\n \"+ p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2,\",\n \"thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) )\",\n \"/ 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if\",\n \"rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data in\",\n \"= number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot\",\n \"vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude =\",\n \"title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), )\",\n \"of confidence intervals to compute, which must be between 0 and 100 inclusive.\",\n \") fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2,\",\n \"line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} -\",\n \"line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ),\",\n \"with diagrams for frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec):\",\n \"are available: Matplotlib and Bokeh. The user chooses between them using the attribute\",\n \") ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 =\",\n \"\\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1)\",\n \"for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0]\",\n \"to change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure\",\n \"args : optional Additional plot axes kwargs : optional Additional key word arguments\",\n \"\\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =\",\n \"\\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self,\",\n \"is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line,\",\n \"each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude\",\n \"%{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def\",\n \"y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase:\",\n \"with the frequencies, magnitude (dB) of the frequency response for each pair input/output.\",\n \"2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i],\",\n \"%{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"Magnitude of the frequency response for node for each frequency. phase : array\",\n \"System response. xout : array Time evolution of the state vector. nodes_list: array\",\n \"enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j],\",\n \"j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[...,\",\n \"probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly.\",\n \"# fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe,\",\n \"range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1),\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) )\",\n \"2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter(\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[],\",\n \"object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile =\",\n \"percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p\",\n \"magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase\",\n \"- Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) )\",\n \"+ 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\",\",\n \"go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile(\",\n \"**kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the unbalance response magnitude. Parameters\",\n \"fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis,\",\n \"/ 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace(\",\n \"Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],\",\n \"x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0,\",\n \"optional harmonics : list, optional List with the harmonics to be plotted. The\",\n \"the speed range in rad/s. wd : array Array with the damped natural\",\n \"for Frequency Response. Parameters ---------- speed_range : array Array with the speed range\",\n \"---------- node : int Select the node to display the respective orbit response.\",\n \"with the plot. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs\",\n \"enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j],\",\n \"y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof\",\n \"data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False\",\n \"color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p\",\n \"plot. \\\"\\\"\\\" if fig is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval\",\n \"plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram.\",\n \"{i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence\",\n \"= magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude(\",\n \"percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", )\",\n \"(dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v\",\n \": optional Additional plot axes kwargs : optional Additional key word arguments can\",\n \"self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe,\",\n \"only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more information.\",\n \":, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx],\",\n \") ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe,\",\n \"self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def\",\n \"%{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x,\",\n \") return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs\",\n \"subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis,\",\n \"name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, )\",\n \"forced response using Plotly. Parameters ---------- probe : list of tuples List with\",\n \"p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1)\",\n \"an input using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"\\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"[{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in\",\n \"and orientations. Parameters ---------- probe : list of tuples List with tuples (node,\",\n \") ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *\",\n \"p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50\",\n \"self.yout[..., ndof * n + 1], 50 + p / 2, axis=0 ),\",\n \"fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes(\",\n \"in a 3D view. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\",\",\n \"in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter(\",\n \"refers to +X direction. percentile : list, optional Sequence of percentiles to compute,\",\n \"\\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v\",\n \"unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments can\",\n \"object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units\",\n \"%{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig\",\n \"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,\",\n \"plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the frequency\",\n \"self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots\",\n \"state vector. nodes_list: array list with nodes from a rotor model. nodes_pos: array\",\n \"of freedom to observe the response. percentile : list, optional Sequence of percentiles\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p,\",\n \"harmonics to be plotted. The default is to plot 1x. kwargs : optional\",\n \"z=np.percentile( self.yout[..., ndof * n + 1], 50 + p / 2, axis=0\",\n \"dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1])\",\n \"fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label,\",\n \"- confidence interval: {p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"),\",\n \"0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:,\",\n \"= operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle)\",\n \"/ 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 +\",\n \"p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence\",\n \"50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node +\",\n \"= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace(\",\n \"- Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for\",\n \"Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs,\",\n \"the phase response given a set of probes. Parameters ---------- probe : list\",\n \": int Select the node to display the respective orbit response. percentile :\",\n \"x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=(\",\n \"** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3,\",\n \"force_resp : array Array with the force response for each node for each\",\n \"as go from plotly import io as pio from plotly.subplots import make_subplots from\",\n \"fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j\",\n \"= np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2, axis=0)\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"located. orientation : float, probe orientation angle about the shaft. The 0 refers\",\n \"harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile :\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\",\",\n \"object with the plot. kwargs : optional Additional key word arguments can be\",\n \"fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase\",\n \"each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly\",\n \"%{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude,\",\n \"array Array with the force response for each node for each frequency. frequency_range\",\n \"\\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on for data in fig0[\\\"data\\\"]:\",\n \"%{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p\",\n \"j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude:\",\n \"figure object with the plot. kwargs : optional Additional key word arguments can\",\n \"_probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) #\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50\",\n \"must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure\",\n \"frequencies, magnitude (dB) of the frequency response for each pair input/output. phase :\",\n \"fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- probe\",\n \"self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude\",\n \"operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:,\",\n \"0 color_i = 0 for i, p in enumerate(probe): dofx = p[0] *\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"array System response. xout : array Time evolution of the state vector. nodes_list:\",\n \"= probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]),\",\n \"and provide plots for Time Response and Orbit Response. Parameters ---------- time_range :\",\n \"Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile : list, optional\",\n \"None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0,\",\n \"node for each frequency. frequency_range : array Array with the frequencies. magnitude :\",\n \"np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p\",\n \"* self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1] #\",\n \") for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50\",\n \"percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the phase response\",\n \"**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 +\",\n \"in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0]\",\n \"express as px from plotly import graph_objects as go from plotly import io\",\n \"np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,\",\n \"plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range =\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if units ==\",\n \"line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" +\",\n \"enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude,\",\n \"response. percentile : list, optional Sequence of percentiles to compute, which must be\",\n \": Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\"\",\n \"make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette of\",\n \"50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\",\",\n \") fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), )\",\n \"row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis,\",\n \"nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list =\",\n \"must be between 0% and 100% inclusive. units : str, optional Unit system\",\n \") return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Time\",\n \"p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" +\",\n \"axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else\",\n \"= np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 +\",\n \"change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference\",\n \"shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object with\",\n \"axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase,\",\n \"p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2\",\n \"1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1\",\n \"subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in\",\n \"color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\"\",\n \"x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" +\",\n \"\\\"\\\"\\\"Store stochastic results and provide plots for Time Response and Orbit Response. Parameters\",\n \"response for each node for each frequency. frequency_range : array Array with the\",\n \"of confidence intervals to compute, which must be between 0% and 100% inclusive.\",\n \"interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\",\",\n \"for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode\",\n \"figure object with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list,\",\n \"+ \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label)\",\n \"and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals to compute,\",\n \"conf_interval : list, optional Sequence of confidence intervals to compute, which must be\",\n \"2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence\",\n \"magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude(\",\n \"axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1),\",\n \"frequency phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values\",\n \"array Array with the frequencies, phase of the frequency response for each pair\",\n \"np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p\",\n \"Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot\",\n \"fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval:\",\n \"to compute, which must be between 0 and 100 inclusive. units : str,\",\n \"for frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values\",\n \"and provide plots for Forced Response. Parameters ---------- force_resp : array Array with\",\n \"- percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x =\",\n \"go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% -\",\n \"and an input using Plotly. Parameters ---------- percentile : list, optional Sequence of\",\n \": array Time evolution of the state vector. nodes_list: array list with nodes\",\n \"of freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly\",\n \"axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, )\",\n \"go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\"\",\n \"must be between 0 and 100 inclusive. conf_interval : list, optional Sequence of\",\n \"array. yout : array System response. xout : array Time evolution of the\",\n \"(node, orientation angle). node : int indicate the node where the probe is\",\n \"intervals to compute, which must be between 0 and 100 inclusive. kwargs :\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p,\",\n \"natural frequency. Two options for plooting are available: Matplotlib and Bokeh. The user\",\n \"\\\"\\\"\\\"Plot orbit response (3D). This function plots orbits for each node on the\",\n \"name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\"\",\n \"hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p\",\n \"Diagram. It's possible to visualize multiples harmonics in a single plot to check\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p\",\n \"for each frequency. number_dof = int Number of degrees of freedom per shaft\",\n \"Time array. yout : array System response. xout : array Time evolution of\",\n \"chooses between them using the attribute plot_type. The default is bokeh Parameters ----------\",\n \"fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This\",\n \"go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1}\",\n \"color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\"\",\n \"plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more\",\n \"subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] )\",\n \"= p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0),\",\n \"100 inclusive. kwargs : optional Additional key word arguments can be passed to\",\n \"mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n]\",\n \"opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" +\",\n \"range in rad/s. magnitude : array Array with the frequencies, magnitude (dB) of\",\n \"= np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 -\",\n \"indicate the node where the probe is located. orientation : float, probe orientation\",\n \"inclusive. kwargs : optional Additional key word arguments can be passed to change\",\n \"r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p),\",\n \"go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile:\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6,\",\n \"go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i +\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p),\",\n \"fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1,\",\n \"showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic\",\n \") for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p /\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot\",\n \"nodes_list: array list with nodes from a rotor model. nodes_pos: array Rotor nodes\",\n \"mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval:\",\n \"np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig\",\n \"method plots the time response given a tuple of probes with their nodes\",\n \"2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar(\",\n \"**kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[],\",\n \"): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- dof : int Degree\",\n \"frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \") for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p /\",\n \"graph_objects.Figure() The figure object with the plot. units : str, optional Unit system\",\n \"for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2,\",\n \"color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p\",\n \"forced response using Plotly. Parameters ---------- dof : int Degree of freedom. units\",\n \") return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Forced\",\n \"can be passed to change the plot layout (e.g. width=800, height=600, ...). *See\",\n \") ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode(\",\n \"2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter(\",\n \"0 color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :,\",\n \"{i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=(\",\n \"bokeh Parameters ---------- speed_range : array Array with the speed range in rad/s.\",\n \"\\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec\",\n \"the plot. kwargs : optional Additional key word arguments can be passed to\",\n \"**kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p,\",\n \"fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar\",\n \"width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ),\",\n \"p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False,\",\n \"- confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\")\",\n \"method plots the unbalance response magnitude. Parameters ---------- probe : list of tuples\",\n \"col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\",\",\n \"probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0,\",\n \"\\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):\",\n \"np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean\",\n \"{}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\"\",\n \"name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs,\",\n \"in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar(\",\n \": list, optional Sequence of confidence intervals to compute, which must be between\",\n \"self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /\",\n \"in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase\",\n \"axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5,\",\n \"- Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace(\",\n \"kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace(\",\n \"100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units\",\n \"data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis,\",\n \"y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] =\",\n \"legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j,\",\n \"the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams\",\n \"- confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\")\",\n \"phase : array Phase of the frequency response for node for each frequency.\",\n \"borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle\",\n \"\\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig\",\n \"%{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor\",\n \"= self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots(\",\n \"magnitude given an output and an input using Plotly. Parameters ---------- percentile :\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and\",\n \"fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return\",\n \"optional List with the harmonics to be plotted. The default is to plot\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\",\",\n \"subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout(\",\n \"interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ),\",\n \"returns graphs for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6,\",\n \"= np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[...,\",\n \"------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" conf_interval\",\n \"- np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j,\",\n \"theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase:\",\n \"= number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot\",\n \"+ _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range,\",\n \"np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])),\",\n \"percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2,\",\n \"conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude\",\n \"axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y -\",\n \"is None: fig = go.Figure() color_p = 0 color_i = 0 for i,\",\n \"int Number of degrees of freedom per shaft element's node Returns ------- fig\",\n \"= p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), +\",\n \"list, optional Sequence of confidence intervals to compute, which must be between 0\",\n \"axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1,\",\n \"which must be between 0 and 100 inclusive. harmonics: list, optional List withe\",\n \"node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node],\",\n \"y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude:\",\n \"+ \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args,\",\n \"+ p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2,\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"\",\n \"the time response given a tuple of probes with their nodes and orientations.\",\n \"), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram.\",\n \"plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function\",\n \"+ \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"),\",\n \"legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]):\",\n \"Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\"\",\n \"0 and 100 inclusive. harmonics: list, optional List withe the harmonics to be\",\n \"{}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range,\",\n \"legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =\",\n \"confidence intervals to compute, which must be between 0 and 100 inclusive. kwargs\",\n \"freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure\",\n \"= go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency:\",\n \"conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}],\",\n \"pair input/output. phase : array Array with the frequencies, phase of the frequency\",\n \"), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec\",\n \"for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0),\",\n \"- p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j],\",\n \"100 inclusive. harmonics: list, optional List withe the harmonics to be plotted. The\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if fig is\",\n \"height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode(\",\n \"title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\",\",\n \"j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2, axis=0)\",\n \"evolution of the state vector. nodes_list: array list with nodes from a rotor\",\n \"stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range : array\",\n \"Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\")\",\n \"2D view. Parameters ---------- node : int Select the node to display the\",\n \"interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\",\",\n \"self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0,\",\n \"Frequency Response. Parameters ---------- speed_range : array Array with the speed range in\",\n \"# fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] )\",\n \"arguments can be passed to change the plot layout (e.g. width=800, height=600, ...).\",\n \"p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50\",\n \"\\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response given a\",\n \"), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for\",\n \"fmt: on for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"**kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs,\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \") for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d(\",\n \"Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which must\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p,\",\n \"the respective orbit response. percentile : list, optional Sequence of percentiles to compute,\",\n \"- confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", )\",\n \"{p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs,\",\n \"p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"),\",\n \"row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900,\",\n \"= self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,\",\n \"x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\"\",\n \"50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1],\",\n \"output and an input. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"axes kwargs : optional Additional key word arguments can be passed to change\",\n \"n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\",\",\n \"with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range\",\n \"color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False,\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for\",\n \"np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x =\",\n \"j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j]\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p,\",\n \"fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]),\",\n \"object with the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\"\",\n \"2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter(\",\n \"x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\",\",\n \"and 100 inclusive. harmonics: list, optional List withe the harmonics to be plotted.\",\n \"height=800, ...). *See Plotly Python Figure Reference for more information. Returns ------- fig\",\n \"= dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval,\",\n \"{\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on for data in\",\n \"rad/s. magnitude : array Array with the frequencies, magnitude (dB) of the frequency\",\n \"+ 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude:\",\n \"is bokeh Parameters ---------- speed_range : array Array with the speed range in\",\n \") fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"),\",\n \"%{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"),\",\n \"polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2,\",\n \"def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This\",\n \"their nodes and orientations. Parameters ---------- probe : list of tuples List with\",\n \"ndof * n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p),\",\n \"units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\":\",\n \"provide plots for Frequency Response. Parameters ---------- speed_range : array Array with the\",\n \"data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1)\",\n \"* node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3,\",\n \"fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1)\",\n \"Parameters ---------- node : int Select the node to display the respective orbit\",\n \"p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1),\",\n \"{}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) )\",\n \"p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p\",\n \"v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d(\",\n \"fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots\",\n \") for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n],\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif\",\n \"default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[...,\",\n \"*args, **kwargs ): \\\"\\\"\\\"Plot time response. This method plots the time response given\",\n \"The default is to plot 1x. kwargs : optional Additional key word arguments\",\n \"wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self,\",\n \"be between 0% and 100% inclusive. units : str, optional Unit system Default\",\n \"yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof\",\n \"Select the node to display the respective orbit response. percentile : list, optional\",\n \"conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots orbits for\",\n \"self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[],\",\n \"(μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots\",\n \"enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase,\",\n \"dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] *\",\n \"theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude:\",\n \"np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,\",\n \"the rotor system in a 3D view. Parameters ---------- percentile : list, optional\",\n \"mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle))\",\n \"line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1}\",\n \"------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle\",\n \"plooting are available: Matplotlib and Bokeh. The user chooses between them using the\",\n \"default is to plot 1x. kwargs : optional Additional key word arguments can\",\n \") x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,\",\n \"px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for Campbell Diagram. It's\",\n \"ndof * node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof\",\n \"customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe\",\n \"axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase,\",\n \"\\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =\",\n \"**kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- dof : int\",\n \"units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{},\",\n \"+ p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50\",\n \"the plot layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for\",\n \"+ 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i +=\",\n \"response. This method plots the phase response given an output and an input\",\n \"/ 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace(\",\n \"Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store\",\n \"50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p /\",\n \"- percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), )\",\n \"ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0),\",\n \") return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This\",\n \"node Returns ------- fig : Plotly graph_objects.Figure() The figure object with the plot.\",\n \"*See Plotly Python Figure Reference for more information. Returns ------- fig : Plotly\",\n \"a single plot to check other speeds which also excite a specific natural\",\n \"z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if\",\n \"**kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile :\",\n \"row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis,\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14),\",\n \"legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using\",\n \"phase response given an output and an input. Parameters ---------- percentile : list,\",\n \"nodes and orientations. Parameters ---------- probe : list of tuples List with tuples\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic\",\n \"percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots orbits\",\n \"%{y:.2e}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p\",\n \"tuple of probes with their nodes and orientations. Parameters ---------- probe : list\",\n \"array Time array. yout : array System response. xout : array Time evolution\",\n \"Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0 =\",\n \"= np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0]\",\n \"{i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i\",\n \"opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False,\",\n \"name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ),\",\n \"amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" # fmt: off fig0 =\",\n \"of degrees of freedom per shaft element's node Returns ------- fig : Plotly\",\n \"self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig\",\n \"Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self,\",\n \"dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd\",\n \"color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position:\",\n \"), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot\",\n \"and phase response given a set of probes. Parameters ---------- dof : int\",\n \"axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase =\",\n \"mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ),\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i,\",\n \"percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range,\",\n \"node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node\",\n \"\\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v\",\n \"import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default\",\n \"inclusive. args: optional harmonics : list, optional List with the harmonics to be\",\n \"1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"results and provide plots for Frequency Response. Parameters ---------- speed_range : array Array\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"output and an input using Plotly. Parameters ---------- percentile : list, optional Sequence\",\n \"y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n in\",\n \"legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes(\",\n \"frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude\",\n \"about the shaft. The 0 refers to +X direction. percentile : list, optional\",\n \"[{}, None]] ) # fmt: on for data in fig0[\\\"data\\\"]: data.showlegend = False\",\n \"units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label =\",\n \"return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Forced Response.\",\n \"name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal\",\n \"response given a set of probes. Parameters ---------- probe : list of tuples\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range,\",\n \"**kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 =\",\n \"v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace(\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots =\",\n \"p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50\",\n \"percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) )\",\n \"Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range,\",\n \"/ 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=(\",\n \"axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval:\",\n \"legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p in\",\n \"+= 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 =\",\n \"from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set\",\n \"figure with diagrams for frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd,\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5,\",\n \"), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\"\",\n \"legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" +\",\n \"<reponame>JuliaMota/ross<filename>ross/stochastic/st_results.py<gh_stars>0 \\\"\\\"\\\"STOCHASTIC ROSS plotting module. This module returns graphs for each type of\",\n \"def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency.\",\n \"np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])),\",\n \"which must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The\",\n \"Parameters ---------- speed_range : array Array with the speed range in rad/s. wd\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" default_values =\",\n \"ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0),\",\n \"must be between 0 and 100 inclusive. units : str, optional Unit system\",\n \"+ 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"as px from plotly import graph_objects as go from plotly import io as\",\n \"h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"),\",\n \"50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof * node +\",\n \"0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j,\",\n \"plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly.\",\n \"list, optional Sequence of percentiles to compute, which must be between 0 and\",\n \"False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1,\",\n \"frequencies. magnitude : array Magnitude of the frequency response for node for each\",\n \"enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n],\",\n \"2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter(\",\n \"- Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency:\",\n \"axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5,\",\n \"force response for each node for each frequency. frequency_range : array Array with\",\n \"self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This method\",\n \"response (3D). This function plots orbits for each node on the rotor system\",\n \"x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"{}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs,\",\n \"Array with the force response for each node for each frequency. frequency_range :\",\n \"This method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of\",\n \"make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]:\",\n \"Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile,\",\n \"Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" # fmt:\",\n \"x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50\",\n \"axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i\",\n \"fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return\",\n \"to +X direction. percentile : list, optional Sequence of percentiles to compute, which\",\n \"p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i\",\n \"{i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase:\",\n \"fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1}\",\n \"= np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) #\",\n \"fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig =\",\n \"subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Time Response and\",\n \"to compute, which must be between 0% and 100% inclusive. units : str,\",\n \"data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2)\",\n \"array Array with the frequencies, magnitude (dB) of the frequency response for each\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"---------- speed_range : array Array with the speed range in rad/s. wd :\",\n \"(μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig =\",\n \"interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\",\",\n \"number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time\",\n \"in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof +\",\n \"Parameters ---------- force_resp : array Array with the force response for each node\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile:\",\n \"phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range\",\n \"color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs,\",\n \"fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ):\",\n \"opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"= nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args,\",\n \"+ 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else\",\n \"for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1,\",\n \"if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\":\",\n \"+ \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in\",\n \"percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase:\",\n \"subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout(\",\n \"\\\"\\\"\\\"Store stochastic results and provide plots for Forced Response. Parameters ---------- force_resp :\",\n \"for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from\",\n \"dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k,\",\n \"fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label,\",\n \"- angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]),\",\n \"p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p),\",\n \"borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell\",\n \"percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range,\",\n \"%{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x,\",\n \"response given a set of probes. Parameters ---------- dof : int Degree of\",\n \"color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i,\",\n \"angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0),\",\n \"i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] *\",\n \") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0),\",\n \"{p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return\",\n \"i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p\",\n \"v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"),\",\n \"between 0 and 100 inclusive. args: optional harmonics : list, optional List with\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node,\",\n \"figure object with the plot. units : str Magnitude unit system. Default is\",\n \"np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p\",\n \"0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:,\",\n \"percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters ----------\",\n \"%{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor\",\n \"probes. Parameters ---------- dof : int Degree of freedom to observe the response.\",\n \"p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"speed_range : array Array with the speed range in rad/s. wd : array\",\n \"legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1],\",\n \"x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} -\",\n \"%{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j,\",\n \"_probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp,\",\n \"j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j\",\n \"axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" +\",\n \"with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude,\",\n \"def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response.\",\n \"plot 1x. kwargs : optional Additional key word arguments can be passed to\",\n \"with the plot. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs\",\n \") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node],\",\n \"go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node +\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1),\",\n \"be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object\",\n \"** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs\",\n \"j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5,\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"/ 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace(\",\n \"p[0] * self.number_dof + 1 angle = p[1] # fmt: off operator =\",\n \"in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof *\",\n \"\\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,\",\n \"def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response.\",\n \"- percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x =\",\n \"{}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"= log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies\",\n \"{i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", ) ) color_i +=\",\n \"np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p\",\n \"%{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range,\",\n \"p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile:\",\n \"graph_objects.Figure() The figure object with the plot. units : str Magnitude unit system.\",\n \"self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\":\",\n \"/ 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3\",\n \"*args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots orbits for a given\",\n \"/ 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4\",\n \"self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data,\",\n \"= int Number of degrees of freedom per shaft element's node. Returns -------\",\n \"), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof\",\n \"node where the probe is located. orientation : float, probe orientation angle about\",\n \"line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i,\",\n \"(dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig =\",\n \"probes. Parameters ---------- probe : list of tuples List with tuples (node, orientation\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1}\",\n \"line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for\",\n \"xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout\",\n \"fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i\",\n \"+ p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1],\",\n \"object with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval =\",\n \"legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ),\",\n \"fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if units\",\n \"{}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs,\",\n \"conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1,\",\n \"p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True\",\n \"\\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp,\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile =\",\n \"palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic\",\n \"p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2\",\n \"subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return\",\n \"col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14),\",\n \"must be between 0 and 100 inclusive. harmonics: list, optional List withe the\",\n \"v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace(\",\n \"default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile,\",\n \"with the harmonics to be plotted. The default is to plot 1x. kwargs\",\n \"**kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1\",\n \"x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p,\",\n \"showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y -\",\n \"**kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots orbits for each node on\",\n \"go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} -\",\n \"frequency phase angle vs frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile,\",\n \"\\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k,\",\n \"for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams\",\n \"shaft. The 0 refers to +X direction. percentile : list, optional Sequence of\",\n \"%{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace(\",\n \"percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is\",\n \"50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p),\",\n \"1-dimensional array Time array. yout : array System response. xout : array Time\",\n \"interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\",\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i +\",\n \"go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency:\",\n \"i < 0 else i for i in aux_phase] ) angle = p[1]\",\n \"= np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3,\",\n \"+ 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence\",\n \"axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=(\",\n \"= self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2\",\n \"amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units,\",\n \"...). *See Plotly Python Figure Reference for more information. Returns ------- fig :\",\n \"conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots the phase response given\",\n \"y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p,\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs):\",\n \"list with nodes from a rotor model. nodes_pos: array Rotor nodes axial positions.\",\n \"be between 0 and 100 inclusive. harmonics: list, optional List withe the harmonics\",\n \"**kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots the phase response given an\",\n \": Plotly graph_objects.Figure() The figure object with the plot. kwargs : optional Additional\",\n \"= 0 color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:,\",\n \"enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"),\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"), **kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,\",\n \"plots the frequency and phase response given a set of probes. Parameters ----------\",\n \"line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency:\",\n \"r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i +\",\n \"attribute plot_type. The default is bokeh Parameters ---------- speed_range : array Array with\",\n \"self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos\",\n \"stochastic results and provide plots for Campbell Diagram. It's possible to visualize multiples\",\n \"conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{},\",\n \"= go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter(\",\n \"z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list):\",\n \"%{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode\",\n \"default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2)\",\n \"+ p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2,\",\n \"x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j),\",\n \": array Array with the force response for each node for each frequency.\",\n \"be between 0 and 100 inclusive. kwargs : optional Additional key word arguments\",\n \"def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec =\",\n \") probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp =\",\n \"\\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in\",\n \"p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\",\",\n \"frequency response magnitude given an output and an input using Plotly. Parameters ----------\",\n \"np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp\",\n \"Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments can be passed\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1),\",\n \"+ 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1)\",\n \"fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] )\",\n \"for each frequency. frequency_range : array Array with the frequencies. magnitude : array\",\n \"= \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile\",\n \"{i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency:\",\n \"to compute, which must be between 0% and 100% inclusive. fig : Plotly\",\n \"io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default =\",\n \"== \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if\",\n \"in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly import express as px\",\n \"), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency\",\n \"vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp\",\n \"return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot\",\n \"): \\\"\\\"\\\"Plot frequency response. This method plots the unbalance response magnitude. Parameters ----------\",\n \"ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for\",\n \"frequency. Two options for plooting are available: Matplotlib and Bokeh. The user chooses\",\n \"= p[0] * self.number_dof + 1 angle = p[1] # fmt: off operator\",\n \"\\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace(\",\n \"fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots\",\n \"color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe\",\n \"Sequence of confidence intervals to compute, which must be between 0% and 100%\",\n \"+ 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence\",\n \"plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This\",\n \"< 0 else i for i in aux_phase] ) angle = p[1] probe_phase[i]\",\n \"node : int indicate the node where the probe is located. orientation :\",\n \"height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self,\",\n \"off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval,\",\n \"fig = go.Figure() color_p = 0 color_i = 0 for i, p in\",\n \"fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for\",\n \"\\\"\\\"\\\" if fig is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval =\",\n \"a specific natural frequency. Two options for plooting are available: Matplotlib and Bokeh.\",\n \"given an output and an input using Plotly. Parameters ---------- percentile : list,\",\n \"frequency. number_dof = int Number of degrees of freedom per shaft element's node.\",\n \"Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict(\",\n \"with the frequencies. magnitude : array Magnitude of the frequency response for node\",\n \"color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x\",\n \"phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values =\",\n \"\\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval =\",\n \"---------- percentile : list, optional Sequence of percentiles to compute, which must be\",\n \"plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots\",\n \"= \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for\",\n \"wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped\",\n \"fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data\",\n \"fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i\",\n \"= np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"Parameters ---------- dof : int Degree of freedom to observe the response. percentile\",\n \"using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"visualize multiples harmonics in a single plot to check other speeds which also\",\n \"line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\",\",\n \"+ 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in\",\n \"in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j],\",\n \"vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for\",\n \"units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval,\",\n \"node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency\",\n \"self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 =\",\n \"enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase =\",\n \"for node for each frequency. phase : array Phase of the frequency response\",\n \"the plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile\",\n \"== \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude\",\n \"natural frequencies log_dec : array Array with the Logarithmic decrement Returns ------- subplots\",\n \"= go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency:\",\n \"None: fig = go.Figure() color_i = 0 color_p = 0 for i, p\",\n \"magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[...,\",\n \"= np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units ==\",\n \"x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0),\",\n \"of freedom per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The\",\n \"node + 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof\",\n \"color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x\",\n \"x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1}\",\n \"2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter(\",\n \": int indicate the node where the probe is located. orientation : float,\",\n \"The figure object with the plot. args : optional Additional plot axes kwargs\",\n \"y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof\",\n \"plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k,\",\n \"inclusive. conf_interval : list, optional Sequence of confidence intervals to compute, which must\",\n \"Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i,\",\n \"title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic\",\n \"row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ),\",\n \"Parameters ---------- time_range : 1-dimensional array Time array. yout : array System response.\",\n \"with the frequencies, phase of the frequency response for each pair input/output. Returns\",\n \": int Degree of freedom to observe the response. percentile : list, optional\",\n \") return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Frequency\",\n \"using Plotly. Parameters ---------- probe : list of tuples List with tuples (node,\",\n \"and Bokeh. The user chooses between them using the attribute plot_type. The default\",\n \"x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50\",\n \"Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2\",\n \"orientation : float, probe orientation angle about the shaft. The 0 refers to\",\n \"# fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase):\",\n \"{}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\",\",\n \"np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p\",\n \"in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6,\",\n \"method plots the frequency and phase response given a set of probes. Parameters\",\n \"col=1) for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data\",\n \"Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900,\",\n \"self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters\",\n \"List with tuples (node, orientation angle). node : int indicate the node where\",\n \"---------- force_resp : array Array with the force response for each node for\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i),\",\n \"operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:,\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range,\",\n \"in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p /\",\n \"percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile :\",\n \"\\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):\",\n \"opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude:\",\n \"i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in\",\n \"def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This\",\n \"confidence intervals to compute, which must be between 0 and 100 inclusive. args:\",\n \"p1 = np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p),\",\n \"self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p /\",\n \"hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\",\",\n \"with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" # fmt: off fig0\",\n \"degrees of freedom per shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots()\",\n \"self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof\",\n \"percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure()\",\n \"ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof *\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile:\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p\",\n \"= np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :,\",\n \"1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,\",\n \"figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile,\",\n \"percentiles to compute, which must be between 0 and 100 inclusive. conf_interval :\",\n \"100% inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs :\",\n \"color_p = 0 color_i = 0 for i, p in enumerate(probe): probe_phase =\",\n \"go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"%{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p /\",\n \"more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for\",\n \"radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ),\",\n \") return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar\",\n \"{i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"plotted. The default is to plot 1x. kwargs : optional Additional key word\",\n \"plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile =\",\n \"self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None,\",\n \"- percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\",\",\n \") ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean\",\n \"import graph_objects as go from plotly import io as pio from plotly.subplots import\",\n \"Figure Reference for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure\",\n \"response for each pair input/output. phase : array Array with the frequencies, phase\",\n \"of the state vector. nodes_list: array list with nodes from a rotor model.\",\n \"line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p in\",\n \"{p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1]))\",\n \"axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile:\",\n \"a set of probes. Parameters ---------- dof : int Degree of freedom to\",\n \"**kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for\",\n \"------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" def\",\n \"fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2)\",\n \"This method plots the frequency and phase response given a set of probes.\",\n \"interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\")\",\n \"= phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi\",\n \"n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6,\",\n \"Degree of freedom to observe the response. percentile : list, optional Sequence of\",\n \"p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\",\",\n \"enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5,\",\n \"if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node],\",\n \"be passed to change the plot layout (e.g. width=800, height=600, ...). *See Plotly\",\n \"dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k,\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide\",\n \"node on the rotor system in a 3D view. Parameters ---------- percentile :\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y -\",\n \"axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase,\",\n \"col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1,\",\n \"/ 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i],\",\n \"go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\",\",\n \"legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p\",\n \"phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self,\",\n \"* n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]),\",\n \"default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h\",\n \"Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude -\",\n \"fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis,\",\n \"st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly import express as px from\",\n \"opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1}\",\n \"time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos\",\n \"conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile : list,\",\n \"size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return\",\n \"y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency:\",\n \"j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h),\",\n \"is \\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments can be passed to\",\n \"* np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt:\",\n \"), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 -\",\n \"be plotted. The default is to plot 1x. kwargs : optional Additional key\",\n \"0 and 100 inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\"\",\n \"mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50\",\n \"angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude\",\n \"go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1],\",\n \"plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig\",\n \"for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] =\",\n \"/ 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\",\",\n \"Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return\",\n \"y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} -\",\n \"angle vs frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units,\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof *\",\n \"row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis,\",\n \"* node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1,\",\n \"go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase,\",\n \"probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the phase\",\n \"angle about the shaft. The 0 refers to +X direction. percentile : list,\",\n \"@ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 +\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof\",\n \"List with the harmonics to be plotted. The default is to plot 1x.\",\n \"ROSS plotting module. This module returns graphs for each type of analyses in\",\n \"1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[],\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"- Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"+= 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def\",\n \"in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis,\",\n \"confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", ) )\",\n \"{i + 1} - confidence interval: {p}%\\\", ) ) color_i += 1 fig.update_layout(\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i =\",\n \"np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,\",\n \"hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) )\",\n \"axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe\",\n \"subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict(\",\n \"v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line\",\n \"conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots orbits for\",\n \"can be passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See\",\n \") for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6,\",\n \"frequency response. This method plots the frequency and phase response given a set\",\n \"def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase =\",\n \"\\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- probe : list of tuples\",\n \"units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\"\",\n \"%{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with\",\n \"plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method\",\n \"x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" +\",\n \"legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" +\",\n \"p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j\",\n \"** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase =\",\n \"borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots\",\n \"else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig\",\n \"vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range,\",\n \"+ 1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), -\",\n \"ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j\",\n \") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6,\",\n \"+ \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar(\",\n \"col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict(\",\n \"name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log\",\n \"dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label =\",\n \"for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1)\",\n \"plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range\",\n \"compute, which must be between 0% and 100% inclusive. fig : Plotly graph_objects.Figure()\",\n \"= np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 +\",\n \"(_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0),\",\n \"an output and an input using Plotly. Parameters ---------- percentile : list, optional\",\n \"Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh\",\n \"to compute, which must be between 0 and 100 inclusive. args: optional harmonics\",\n \"\\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p +=\",\n \"class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Frequency Response. Parameters ----------\",\n \"color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ),\",\n \"+ \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1\",\n \"confidence interval: {p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"),\",\n \"+ 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\"\",\n \"+ \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p\",\n \") fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h,\",\n \"%{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots the frequency\",\n \"if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos,\",\n \"Campbell Diagram. It's possible to visualize multiples harmonics in a single plot to\",\n \"return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D).\",\n \"**kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig\",\n \"title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped\",\n \"i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", )\",\n \"yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig class\",\n \"for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time\",\n \"dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle)\",\n \"plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile\",\n \"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in\",\n \"\\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase\",\n \"%{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" #\",\n \"for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x\",\n \"system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments can be\",\n \"= np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase, 50 +\",\n \"array Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly\",\n \"= self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\",\",\n \"= 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for\",\n \"Array with the frequencies, magnitude (dB) of the frequency response for each pair\",\n \"axes with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\"\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs):\",\n \":, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof]\",\n \"__init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout\",\n \"\\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response given an\",\n \"go.Figure() color_i = 0 color_p = 0 for i, p in enumerate(probe): dofx\",\n \"and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot.\",\n \"Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for\",\n \"vector. nodes_list: array list with nodes from a rotor model. nodes_pos: array Rotor\",\n \"y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"method plots the frequency and phase response given an output and an input.\",\n \"vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range\",\n \"figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp,\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency:\",\n \"color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"),\",\n \"with the speed range in rad/s. magnitude : array Array with the frequencies,\",\n \"color_i = 0 color_p = 0 for i, p in enumerate(probe): dofx =\",\n \"response for node for each frequency. phase : array Phase of the frequency\",\n \"+ 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval:\",\n \"probes with their nodes and orientations. Parameters ---------- probe : list of tuples\",\n \"if fig is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval)\",\n \"\\\"\\\"\\\"Store stochastic results and provide plots for Campbell Diagram. It's possible to visualize\",\n \"n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode( self,\",\n \"default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos))\",\n \"= self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots(\",\n \") fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes(\",\n \"name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs,\",\n \"node : int Select the node to display the respective orbit response. percentile\",\n \"damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence of\",\n \"the speed range in rad/s. magnitude : array Array with the frequencies, magnitude\",\n \"{p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs),\",\n \"+ \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase\",\n \"%{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval):\",\n \"fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, )\",\n \"- Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout(\",\n \"inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional\",\n \": array Magnitude of the frequency response for node for each frequency. phase\",\n \"return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot\",\n \"percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This method plots the\",\n \"i for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] -\",\n \") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p\",\n \"*args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1\",\n \"hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, )\",\n \"p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2,\",\n \"go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j],\",\n \"50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]),\",\n \"line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\",\",\n \"0 and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals to\",\n \"2 * np.pi if i < 0 else i for i in aux_phase]\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" def __init__(self, time_range,\",\n \"optional Additional key word arguments can be passed to change the plot (e.g.\",\n \"which must be between 0 and 100 inclusive. units : str, optional Unit\",\n \"data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2)\",\n \"frequency response. This method plots the unbalance response magnitude. Parameters ---------- probe :\",\n \"**kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence\",\n \"with diagrams for frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args,\",\n \"- Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) )\",\n \"**kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- probe : list\",\n \"return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Frequency Response.\",\n \"def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the\",\n \"p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]),\",\n \"ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette of colors colors1\",\n \"cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data in fig0[\\\"data\\\"]:\",\n \"p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1)\",\n \"response. This method plots the frequency and phase response given an output and\",\n \"for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1,\",\n \"function plots orbits for a given node on the rotor system in a\",\n \"fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0),\",\n \"Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object\",\n \"dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) **\",\n \"fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs,\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i),\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i\",\n \"the frequency and phase response given a set of probes. Parameters ---------- dof\",\n \"= \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else:\",\n \"100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if\",\n \"[[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for\",\n \"frequency. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]),\",\n \"plotly import graph_objects as go from plotly import io as pio from plotly.subplots\",\n \"\\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- dof : int Degree of\",\n \"in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j +\",\n \"the unbalance response magnitude. Parameters ---------- probe : list of tuples List with\",\n \"opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i +\",\n \"the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range =\",\n \"np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace(\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i),\",\n \"* n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]),\",\n \"---------- speed_range : array Array with the speed range in rad/s. magnitude :\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[],\",\n \"input using bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i +\",\n \"conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- dof\",\n \"of the frequency response for node for each frequency. phase : array Phase\",\n \"with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval)\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]),\",\n \"object with the plot. \\\"\\\"\\\" if fig is None: fig = go.Figure() default_values\",\n \"frequency. frequency_range : array Array with the frequencies. magnitude : array Magnitude of\",\n \"= \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval\",\n \"confidence intervals to compute, which must be between 0% and 100% inclusive. fig\",\n \"+ \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, )\",\n \"intervals to compute, which must be between 0 and 100 inclusive. harmonics: list,\",\n \"default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\":\",\n \"the frequency response for node for each frequency. phase : array Phase of\",\n \"(μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k,\",\n \"Sequence of percentiles to compute, which must be between 0 and 100 inclusive.\",\n \"for data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in\",\n \": Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if units ==\",\n \"each node for each frequency. frequency_range : array Array with the frequencies. magnitude\",\n \"col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\",\",\n \"= go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node\",\n \"in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() line =\",\n \"go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]),\",\n \"self.number_dof default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v\",\n \"**kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1\",\n \"speeds which also excite a specific natural frequency. Two options for plooting are\",\n \"str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word\",\n \"for more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude\",\n \"the node to display the respective orbit response. percentile : list, optional Sequence\",\n \"line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude:\",\n \"\\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node\",\n \"axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j ==\",\n \"def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots the\",\n \"for Forced Response. Parameters ---------- force_resp : array Array with the force response\",\n \"number_dof : int Number of degrees of freedom per shaft element's node Returns\",\n \"probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This method plots\",\n \"name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"intervals to compute, which must be between 0 and 100 inclusive. fig :\",\n \") color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50\",\n \"%{y:.2f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase,\",\n \"the harmonics to be plotted. The default is to plot 1x. kwargs :\",\n \"and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False)\",\n \": str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key\",\n \"= np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units\",\n \"+ 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1\",\n \"phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency.\",\n \"* n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True\",\n \"self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ):\",\n \"= np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 +\",\n \"row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), ) return fig\",\n \"= np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range *\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1),\",\n \"col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ),\",\n \") return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This\",\n \"None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof\",\n \"= frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\",\",\n \"go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i\",\n \"fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1,\",\n \"Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"+ \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5,\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1),\",\n \"default is bokeh Parameters ---------- speed_range : array Array with the speed range\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency:\",\n \"inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. kwargs :\",\n \"- np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j,\",\n \"Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d(\",\n \"name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ),\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1),\",\n \") fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2\",\n \"+ 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) )\",\n \"data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis,\",\n \"{}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1\",\n \"yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout =\",\n \"inclusive. harmonics: list, optional List withe the harmonics to be plotted. The default\",\n \") ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X -\",\n \"information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency\",\n \"\\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1)\",\n \"go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe): probe_phase\",\n \"Array with the speed range in rad/s. wd : array Array with the\",\n \"plots the phase response given a set of probes. Parameters ---------- probe :\",\n \"percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots orbits\",\n \"\\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label\",\n \"x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0),\",\n \"%{customdata:.2f}</b>\\\" ), ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p,\",\n \"with nodes from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof\",\n \"enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2,\",\n \"with the plot. args : optional Additional plot axes kwargs : optional Additional\",\n \"interval: {p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ),\",\n \"for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p / 2,\",\n \"**kwargs ): \\\"\\\"\\\"Plot time response. This method plots the time response given a\",\n \"\\\"\\\"\\\" import numpy as np from plotly import express as px from plotly\",\n \"list, optional Sequence of confidence intervals to compute, which must be between 0%\",\n \"col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\",\",\n \"confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude:\",\n \"legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p in\",\n \"p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence\",\n \"x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j),\",\n \"probe : list of tuples List with tuples (node, orientation angle). node :\",\n \"figure object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if\",\n \"def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell\",\n \"confidence intervals to compute, which must be between 0 and 100 inclusive. fig\",\n \"\\\"\\\"\\\"Plot orbit response (2D). This function plots orbits for a given node on\",\n \"%{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i,\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store\",\n \"data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2)\",\n \"50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1,\",\n \"2, axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p\",\n \"np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.magnitude[:, :, 0])\",\n \"with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units ==\",\n \"wd : array Array with the damped natural frequencies log_dec : array Array\",\n \"*See Plotly Python Figure Reference for more information. Returns ------- subplots : Plotly\",\n \"which also excite a specific natural frequency. Two options for plooting are available:\",\n \"response magnitude. Parameters ---------- probe : list of tuples List with tuples (node,\",\n \"- p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1,\",\n \"fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1)\",\n \"The figure object with the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label =\",\n \"is \\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments can be passed to\",\n \": array Array with the frequencies, magnitude (dB) of the frequency response for\",\n \"fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace(\",\n \"percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", )\",\n \"import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette\",\n \"name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\"\",\n \"= np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2 =\",\n \"color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1\",\n \"phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp\",\n \"subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2,\",\n \"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof\",\n \"confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase:\",\n \"between 0 and 100 inclusive. units : str, optional Unit system Default is\",\n \"ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Forced Response. Parameters ---------- force_resp\",\n \"+ 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\",\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1],\",\n \"# set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class\",\n \"@ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2\",\n \"------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" default_values\",\n \"withe the harmonics to be plotted. The default is to plot 1x. kwargs\",\n \"Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for\",\n \"self.number_dof] probe_phase[i] = np.array( [i + 2 * np.pi if i < 0\",\n \"p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2\",\n \"for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2,\",\n \"p, axis=0), y=np.percentile(self.yout[..., ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]),\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node\",\n \"plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors\",\n \"j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter(\",\n \"line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\"\",\n \"= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in\",\n \"1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs):\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range,\",\n \"graph_objects.Figure() The figure object with the plot. args : optional Additional plot axes\",\n \"log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if units == \\\"m\\\":\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide\",\n \"ndof * n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5,\",\n \"= make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) #\",\n \"%{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp,\",\n \"1} - confidence interval: {p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label,\",\n \"color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in\",\n \"for plooting are available: Matplotlib and Bokeh. The user chooses between them using\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0),\",\n \"%{customdata:.2f}</b>\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude,\",\n \"%{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x,\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]),\",\n \"): \\\"\\\"\\\"Plot time response. This method plots the time response given a tuple\",\n \"Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d(\",\n \"* node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof *\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof *\",\n \"color_p = 0 color_i = 0 for i, p in enumerate(probe): dofx =\",\n \"Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200,\",\n \"is located. orientation : float, probe orientation angle about the shaft. The 0\",\n \"the probe is located. orientation : float, probe orientation angle about the shaft.\",\n \"polar_kwargs : optional Additional key word arguments can be passed to change the\",\n \"np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 ) # fmt: on\",\n \"/ 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace(\",\n \"self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof\",\n \"np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp\",\n \"axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1,\",\n \"node on the rotor system in a 2D view. Parameters ---------- node :\",\n \"\\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values =\",\n \"key word arguments can be passed to change the plot layout only (e.g.\",\n \"and 100 inclusive. args: optional harmonics : list, optional List with the harmonics\",\n \"name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(\",\n \"p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"%{y:.2e}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude,\",\n \"units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p),\",\n \"object with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos):\",\n \"**kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots the frequency response magnitude\",\n \"fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i\",\n \"the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"Rotor nodes axial positions. number_dof : int Number of degrees of freedom per\",\n \"showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X\",\n \"nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs\",\n \"elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude\",\n \"of probes. Parameters ---------- dof : int Degree of freedom to observe the\",\n \"title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900,\",\n \"figure object with the plot. units : str, optional Unit system Default is\",\n \"col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), )\",\n \"== \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude\",\n \"Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments can\",\n \"name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\"\",\n \") ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0),\",\n \"speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs):\",\n \"if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label\",\n \"h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3,\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range,\",\n \": Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency.\",\n \"name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude:\",\n \"fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and\",\n \"i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50 + p / 2, axis=1)\",\n \"The 0 refers to +X direction. percentile : list, optional Sequence of percentiles\",\n \"fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots orbits for a\",\n \"go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1],\",\n \"which must be between 0 and 100 inclusive. kwargs : optional Additional key\",\n \"= go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"),\",\n \"np.percentile(self.log_dec[j], 50 + p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p\",\n \"in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6,\",\n \"v) for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy =\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j,\",\n \": float, probe orientation angle about the shaft. The 0 refers to +X\",\n \"fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if units\",\n \"colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and\",\n \"%{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for j, p\",\n \"word arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0,\",\n \"\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\",\",\n \"- confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency:\",\n \"------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" ndof\",\n \"i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor\",\n \"probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[],\",\n \"which must be between 0 and 100 inclusive. conf_interval : list, optional Sequence\",\n \"orientation angle about the shaft. The 0 refers to +X direction. percentile :\",\n \"0 and 100 inclusive. args: optional harmonics : list, optional List with the\",\n \"confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\")\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" conf_interval =\",\n \"make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data\",\n \"/ 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p\",\n \"phase response given a set of probes. Parameters ---------- dof : int Degree\",\n \"linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot\",\n \"go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe): dofx\",\n \"of freedom. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs :\",\n \"Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments can be passed\",\n \"legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j,\",\n \"fig is None: fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line,\",\n \"go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]),\",\n \"which must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict(\",\n \"y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\"\",\n \"frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k,\",\n \") ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14),\",\n \"subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs\",\n \"conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile,\",\n \"pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v)\",\n \"\\\"\\\"\\\"Plot time response. This method plots the time response given a tuple of\",\n \"to compute, which must be between 0 and 100 inclusive. fig : Plotly\",\n \"Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return\",\n \"- Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude\",\n \"It's possible to visualize multiples harmonics in a single plot to check other\",\n \"* np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0),\",\n \"{i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p\",\n \"angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase,\",\n \"single plot to check other speeds which also excite a specific natural frequency.\",\n \"Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The\",\n \"if i < 0 else i for i in aux_phase] ) angle =\",\n \"opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X -\",\n \"title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200,\",\n \"/ 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4\",\n \"fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} -\",\n \"figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" # fmt: off\",\n \"opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=(\",\n \"------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log\",\n \"magnitude. Parameters ---------- probe : list of tuples List with tuples (node, orientation\",\n \"from a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int\",\n \"/ 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p\",\n \"0]) for j, phs in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i]\",\n \"- Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j,\",\n \"time response given a tuple of probes with their nodes and orientations. Parameters\",\n \"fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"),\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j,\",\n \"- Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace(\",\n \"(s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot\",\n \"z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]),\",\n \"opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof *\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]),\",\n \"**kwargs) if fig is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\",\",\n \"* n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0,\",\n \"2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p\",\n \"\\\"rowspan\\\": 2}], [{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for\",\n \"y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval)\",\n \"vs frequency phase angle vs frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe,\",\n \"+ 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency:\",\n \"p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i),\",\n \"the frequencies, phase of the frequency response for each pair input/output. Returns -------\",\n \"array Magnitude of the frequency response for node for each frequency. phase :\",\n \"the frequencies, magnitude (dB) of the frequency response for each pair input/output. phase\",\n \"probe_phase[i] = np.array( [i + 2 * np.pi if i < 0 else\",\n \"array Rotor nodes axial positions. number_dof : int Number of degrees of freedom\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, )\",\n \"50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50\",\n \"numpy as np from plotly import express as px from plotly import graph_objects\",\n \"%{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i,\",\n \"the plot. \\\"\\\"\\\" if fig is None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\")\",\n \"specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data,\",\n \"and 100% inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs\",\n \"/ 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace(\",\n \"p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 -\",\n \"1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1\",\n \"\\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for\",\n \"+= 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None,\",\n \"plot. args : optional Additional plot axes kwargs : optional Additional key word\",\n \"go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\",\",\n \"p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X\",\n \"percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0\",\n \"): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- probe : list of\",\n \"in a 2D view. Parameters ---------- node : int Select the node to\",\n \"This function plots orbits for a given node on the rotor system in\",\n \"= magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs, ):\",\n \"np.percentile(self.log_dec[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1,\",\n \"p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2\",\n \"plots for Frequency Response. Parameters ---------- speed_range : array Array with the speed\",\n \"= np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3 =\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5,\",\n \"amplitude vs frequency. This method plots the frequency response magnitude given an output\",\n \"in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe\",\n \"name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency:\",\n \"axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position:\",\n \"width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig\",\n \"for each pair input/output. phase : array Array with the frequencies, phase of\",\n \"go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase:\",\n \"confidence intervals to compute, which must be between 0 and 100 inclusive. harmonics:\",\n \"self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k,\",\n \"= np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", )\",\n \"be between 0 and 100 inclusive. conf_interval : list, optional Sequence of confidence\",\n \"x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50\",\n \"Matplotlib and Bokeh. The user chooses between them using the attribute plot_type. The\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0,\",\n \"with the plot. kwargs : optional Additional key word arguments can be passed\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_polar_bode( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs,\",\n \") color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return\",\n \"for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5),\",\n \"**kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response\",\n \"go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} -\",\n \"1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" +\",\n \"compute, which must be between 0 and 100 inclusive. kwargs : optional Additional\",\n \"passed to change the plot layout (e.g. width=800, height=600, ...). *See Plotly Python\",\n \"log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs\",\n \"legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" +\",\n \"subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1)\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if fig is None:\",\n \"np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe):\",\n \") color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self,\",\n \"opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns ------- fig\",\n \"orientations. Parameters ---------- probe : list of tuples List with tuples (node, orientation\",\n \"self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n\",\n \"self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None,\",\n \"line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position:\",\n \"compute, which must be between 0% and 100% inclusive. units : str, optional\",\n \"This function plots orbits for each node on the rotor system in a\",\n \"Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label =\",\n \"log_dec : array Array with the Logarithmic decrement Returns ------- subplots : Plotly\",\n \"color_p = 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof\",\n \"using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" +\",\n \"between 0 and 100 inclusive. conf_interval : list, optional Sequence of confidence intervals\",\n \"more information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs\",\n \"fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range * h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j),\",\n \"{}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\",\",\n \"line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i,\",\n \"name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14),\",\n \"\\\"\\\"\\\"Store stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range :\",\n \"- Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1\",\n \"and an input using bokeh. Parameters ---------- percentile : list, optional Sequence of\",\n \": int Number of degrees of freedom per shaft element's node Returns -------\",\n \"opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i +\",\n \"and 100 inclusive. kwargs : optional Additional key word arguments can be passed\",\n \"on the rotor system in a 3D view. Parameters ---------- percentile : list,\",\n \"arguments can be passed to change the plot layout only (e.g. width=1000, height=800,\",\n \"with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is\",\n \"radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[],\",\n \"and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot.\",\n \"for each frequency. phase : array Phase of the frequency response for node\",\n \"self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p /\",\n \"Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs,\",\n \"exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def\",\n \"plots for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array\",\n \"**kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude\",\n \"_probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle))\",\n \"frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs,\",\n \"make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt:\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\",\",\n \"fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout(\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node],\",\n \"in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2,\",\n \"self.xout = xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def\",\n \"color_i = 0 for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0])\",\n \"polar forced response using Plotly. Parameters ---------- probe : list of tuples List\",\n \"magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self,\",\n \"units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1\",\n \"np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p\",\n \"= forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range = frequency_range self.number_dof =\",\n \") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6,\",\n \"given node on the rotor system in a 2D view. Parameters ---------- node\",\n \"subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\",\",\n \"diagrams for frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range\",\n \"the frequency response for each pair input/output. phase : array Array with the\",\n \"borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic\",\n \"mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] =\",\n \"function plots orbits for each node on the rotor system in a 3D\",\n \"kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\",\",\n \"go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\"\",\n \"axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 = np.percentile(self.phase,\",\n \"shaft element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude\",\n \"percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range,\",\n \"height=600, ...). *See Plotly Python Figure Reference for more information. Returns ------- fig\",\n \") for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6,\",\n \"\\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig =\",\n \"font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[],\",\n \"= np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig\",\n \"fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence\",\n \"j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe\",\n \": Plotly graph_objects.Figure() The figure object with the plot. units : str Magnitude\",\n \"y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\"\",\n \"pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if\",\n \"), ) ) color_p += 1 for j, p in enumerate(conf_interval): p1 =\",\n \"with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), ) return\",\n \"fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=(\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if fig is None: fig\",\n \"fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i),\",\n \"frequency response. This method plots the frequency and phase response given an output\",\n \"+ 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude:\",\n \": array Array with the speed range in rad/s. magnitude : array Array\",\n \"fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs)\",\n \"results and provide plots for Forced Response. Parameters ---------- force_resp : array Array\",\n \") for j, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p,\",\n \"0 and 100 inclusive. kwargs : optional Additional key word arguments can be\",\n \"plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800, height=900, polar=dict( radialaxis=fig2.layout.polar.radialaxis, angularaxis=fig2.layout.polar.angularaxis, ), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\",\",\n \"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p,\",\n \"), legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults:\",\n \"2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i],\",\n \"np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p\",\n \"with the damped natural frequencies log_dec : array Array with the Logarithmic decrement\",\n \"customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i\",\n \"fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\",\",\n \"Parameters ---------- dof : int Degree of freedom. units : str Magnitude unit\",\n \"= xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d(\",\n \"hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\",\",\n \"and 100 inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs\",\n \"\\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False),\",\n \"\\\"\\\"\\\"Plot frequency response. This method plots the unbalance response magnitude. Parameters ---------- probe\",\n \"\\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"__init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase\",\n \"units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase\",\n \"y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\")\",\n \"This method plots the phase response given an output and an input using\",\n \"inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. units :\",\n \"input. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"ndof * node + 1], 50 + p / 2, axis=0) p4 =\",\n \"time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout\",\n \"plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig\",\n \"go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1}\",\n \"2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar(\",\n \"color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p\",\n \"data in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]:\",\n \"data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]:\",\n \"harmonics: list, optional List withe the harmonics to be plotted. The default is\",\n \"np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p\",\n \"mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1}\",\n \"def __init__(self, time_range, yout, xout, number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout =\",\n \"return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method\",\n \"polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5,\",\n \"probe orientation angle about the shaft. The 0 refers to +X direction. percentile\",\n \"x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile( self.yout[...,\",\n \"rotor system in a 3D view. Parameters ---------- percentile : list, optional Sequence\",\n \"return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method\",\n \"2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter(\",\n \"axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe\",\n \"%{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j,\",\n \"for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x,\",\n \"ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"),\",\n \"plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly\",\n \") x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase,\",\n \"go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile:\",\n \"row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict(\",\n \"yout : array System response. xout : array Time evolution of the state\",\n \"50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]),\",\n \"units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\"\",\n \"50 - p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p),\",\n \"fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]),\",\n \"fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None,\",\n \"be passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly\",\n \"50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p /\",\n \"plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots the phase\",\n \"(m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label =\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" def __init__(self,\",\n \"%{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[],\",\n \"a 2D view. Parameters ---------- node : int Select the node to display\",\n \"self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using\",\n \"nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[],\",\n \"+ \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"intervals to compute, which must be between 0% and 100% inclusive. units :\",\n \"nodes axial positions. number_dof : int Number of degrees of freedom per shaft\",\n \"for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in\",\n \"return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Time Response\",\n \"xout self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self,\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile\",\n \"is None: fig = go.Figure() color_i = 0 color_p = 0 for i,\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.time_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i\",\n \"y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" +\",\n \"p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2,\",\n \"2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5,\",\n \"kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof\",\n \"else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0),\",\n \"{}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ),\",\n \"axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval:\",\n \"with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude,\",\n \"= np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[..., ndof * node +\",\n \"1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", )\",\n \"between them using the attribute plot_type. The default is bokeh Parameters ---------- speed_range\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. units : str,\",\n \"y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe\",\n \"p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0)\",\n \"y=np.percentile(probe_resp, p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time:\",\n \"\\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional Sequence of\",\n \"array Array with the speed range in rad/s. wd : array Array with\",\n \"%{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self,\",\n \"probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3,\",\n \"with their nodes and orientations. Parameters ---------- probe : list of tuples List\",\n \"fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response (2D).\",\n \"Plotly graph_objects.Figure() The figure object with the plot. args : optional Additional plot\",\n \") ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude -\",\n \"array list with nodes from a rotor model. nodes_pos: array Rotor nodes axial\",\n \"x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1),\",\n \"- Mode {}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"),\",\n \"magnitude : array Array with the frequencies, magnitude (dB) of the frequency response\",\n \"in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0] *\",\n \"bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs):\",\n \"+ 2 * np.pi if i < 0 else i for i in\",\n \": array Array with the Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots()\",\n \"go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 + p / 2, axis=0), z=np.percentile(\",\n \"and provide plots for Campbell Diagram. It's possible to visualize multiples harmonics in\",\n \"graph_objects as go from plotly import io as pio from plotly.subplots import make_subplots\",\n \"np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p\",\n \"hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile):\",\n \"plot axes kwargs : optional Additional key word arguments can be passed to\",\n \"%{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1\",\n \"system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments can be\",\n \"(dB)</b>\\\" if fig is None: fig = go.Figure() color_p = 0 color_i =\",\n \"gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\",\",\n \"fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs):\",\n \"optional Sequence of confidence intervals to compute, which must be between 0% and\",\n \"50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p /\",\n \"row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]:\",\n \"fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs)\",\n \"frequency response for each pair input/output. phase : array Array with the frequencies,\",\n \"= np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :,\",\n \"0 color_p = 0 for i, p in enumerate(probe): dofx = p[0] *\",\n \"{p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1]))\",\n \"colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for Campbell\",\n \"name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x =\",\n \"node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])),\",\n \"= np.array( [i + 2 * np.pi if i < 0 else i\",\n \"---------- dof : int Degree of freedom. units : str Magnitude unit system.\",\n \"subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1)\",\n \"to compute, which must be between 0 and 100 inclusive. kwargs : optional\",\n \"pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs\",\n \"default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\",\",\n \"probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator\",\n \"v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data\",\n \"self.number_dof + 1 angle = p[1] # fmt: off operator = np.array( [[np.cos(angle),\",\n \"vs frequency. This method plots the frequency response magnitude given an output and\",\n \"100 inclusive. args: optional harmonics : list, optional List with the harmonics to\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\",\",\n \"system in a 2D view. Parameters ---------- node : int Select the node\",\n \"%{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j],\",\n \"[np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in\",\n \"p / 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0)\",\n \"None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]]\",\n \"go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p),\",\n \"%{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def\",\n \"\\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on for data in fig0[\\\"data\\\"]: data.showlegend\",\n \"\\\"\\\"\\\"STOCHASTIC ROSS plotting module. This module returns graphs for each type of analyses\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"set of probes. Parameters ---------- probe : list of tuples List with tuples\",\n \"legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for\",\n \"percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0\",\n \"width=800, height=600, ...). *See Plotly Python Figure Reference for more information. Returns -------\",\n \"row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ),\",\n \"p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase):\",\n \"50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\",\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \") # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i +\",\n \"x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} -\",\n \"in rad/s. magnitude : array Array with the frequencies, magnitude (dB) of the\",\n \"y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval:\",\n \") ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in\",\n \"opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns ------- subplots\",\n \"= \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure()\",\n \": list, optional List with the harmonics to be plotted. The default is\",\n \"plot. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional\",\n \"color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs,\",\n \"angle vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False)\",\n \"conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the phase response given\",\n \"self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs,\",\n \"- angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i\",\n \"self.number_dof dofy = p[0] * self.number_dof + 1 angle = p[1] # fmt:\",\n \") color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[],\",\n \"= np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase, 50 -\",\n \"p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50\",\n \"*args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ---------- percentile\",\n \"p / 2, axis=1) p2 = np.percentile(self.log_dec[j], 50 - p / 2, axis=1)\",\n \"np.array( [i + 2 * np.pi if i < 0 else i for\",\n \"in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j +\",\n \"height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self,\",\n \"between 0% and 100% inclusive. fig : Plotly graph_objects.Figure() The figure object with\",\n \"object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig\",\n \"color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" +\",\n \"dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle\",\n \"xout : array Time evolution of the state vector. nodes_list: array list with\",\n \"hoverinfo=\\\"none\\\", **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.log_dec[j], 50\",\n \"Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments can\",\n \"which must be between 0% and 100% inclusive. units : str, optional Unit\",\n \"+ p / 2, axis=1) p4 = np.percentile(self.phase, 50 - p / 2,\",\n \"fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n +\",\n \"The figure object with the plot. kwargs : optional Additional key word arguments\",\n \"provide plots for Campbell Diagram. It's possible to visualize multiples harmonics in a\",\n \"1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for\",\n \"forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase =\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs)\",\n \"y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe\",\n \"= \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None:\",\n \"fig.update_layout(**kwargs) return fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response.\",\n \"def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots\",\n \"The figure object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"/ 2, axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace(\",\n \"the frequency and phase response given an output and an input. Parameters ----------\",\n \"* np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0,\",\n \"plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more\",\n \"np.percentile(probe_resp, 50 + p / 2, axis=0) p2 = np.percentile(probe_resp, 50 - p\",\n \"for each pair input/output. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with\",\n \"- percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x =\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) )\",\n \"exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot( self, probe, percentile=[], conf_interval=[],\",\n \"of tuples List with tuples (node, orientation angle). node : int indicate the\",\n \"probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\",\",\n \"key word arguments can be passed to change the plot layout (e.g. width=800,\",\n \"number_dof = int Number of degrees of freedom per shaft element's node. Returns\",\n \"+ (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp,\",\n \"0 else i for i in aux_phase] ) angle = p[1] probe_phase[i] =\",\n \"2, axis=0) p2 = np.percentile(probe_resp, 50 - p / 2, axis=0) p3 =\",\n \"passed to change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly\",\n \"axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p / 2,\",\n \"= self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]:\",\n \"subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data\",\n \"p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j],\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for\",\n \") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p\",\n \"i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace(\",\n \"np.sin(angle) ** 2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0,\",\n \"harmonics : list, optional List with the harmonics to be plotted. The default\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"units : str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional\",\n \"x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[...,\",\n \"2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 =\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig\",\n \"the damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional Sequence\",\n \"Forced Response. Parameters ---------- force_resp : array Array with the force response for\",\n \"frequency response. This method plots the phase response given a set of probes.\",\n \"50 - p / 2, axis=0) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\",\",\n \"= \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None:\",\n \"using Plotly. Parameters ---------- dof : int Degree of freedom. units : str\",\n \"x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\",\",\n \"= go.Figure() color_i = 0 color_p = 0 for i, p in enumerate(probe):\",\n \"Additional key word arguments can be passed to change the plot (e.g. line=dict(width=4.0,\",\n \"= phase self.frequency_range = frequency_range self.number_dof = number_dof def plot_magnitude( self, probe, percentile=[],\",\n \"( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2 )\",\n \"row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\",\",\n \"specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{}, None]] ) # fmt: on for data\",\n \"= \\\"browser\\\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 =\",\n \"method plots Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) if fig\",\n \"of probes with their nodes and orientations. Parameters ---------- probe : list of\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) for i,\",\n \"\\\"\\\"\\\"Plot amplitude vs frequency. This method plots the frequency response magnitude given an\",\n \"import express as px from plotly import graph_objects as go from plotly import\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot\",\n \"p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]),\",\n \"fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n\",\n \"%{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p in enumerate(percentile): fig.add_trace(\",\n \"The user chooses between them using the attribute plot_type. The default is bokeh\",\n \"p1 = np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2\",\n \"is to plot 1x. kwargs : optional Additional key word arguments can be\",\n \"compute, which must be between 0 and 100 inclusive. harmonics: list, optional List\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p,\",\n \") ) for j, n in enumerate(self.nodes_list): x = np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace(\",\n \"[i + 2 * np.pi if i < 0 else i for i\",\n \"to be plotted. The default is to plot 1x. kwargs : optional Additional\",\n \"p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" +\",\n \"r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\"\",\n \"intervals to compute, which must be between 0 and 100 inclusive. units :\",\n \"color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns -------\",\n \"options for plooting are available: Matplotlib and Bokeh. The user chooses between them\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx =\",\n \"enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0]\",\n \"of probes. Parameters ---------- probe : list of tuples List with tuples (node,\",\n \": array Array with the frequencies, phase of the frequency response for each\",\n \"self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" def __init__(self, time_range, yout, xout,\",\n \"self.number_dof = number_dof def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ):\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig =\",\n \"\\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\"\",\n \"Array with the frequencies, phase of the frequency response for each pair input/output.\",\n \"+ \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2,\",\n \"name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for\",\n \"* n], 50 + p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n\",\n \"module returns graphs for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy\",\n \"response given a tuple of probes with their nodes and orientations. Parameters ----------\",\n \"100 inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. args\",\n \"in a single plot to check other speeds which also excite a specific\",\n \"hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p in enumerate(percentile):\",\n \"orbits for each node on the rotor system in a 3D view. Parameters\",\n \"i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1)\",\n \"opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\",\",\n \"+ np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout):\",\n \"log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd =\",\n \"in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for\",\n \"gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig\",\n \"orbit response (2D). This function plots orbits for a given node on the\",\n \"+= 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[],\",\n \"int Select the node to display the respective orbit response. percentile : list,\",\n \"Logarithmic decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for\",\n \"fig def plot_phase(self, probe, percentile=[], conf_interval=[], fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method\",\n \"- p / 2, axis=1) p3 = np.percentile(self.phase, 50 + p / 2,\",\n \"conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This method plots Campbell Diagram. Parameters ----------\",\n \"fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\",\",\n \"r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p),\",\n \"axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" +\",\n \"Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\"\",\n \") fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\",\",\n \"figure object with the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude\",\n \": array Array with the damped natural frequencies log_dec : array Array with\",\n \"100 inclusive. conf_interval : list, optional Sequence of confidence intervals to compute, which\",\n \"in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: data.showlegend\",\n \"axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 - p /\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.phase, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i),\",\n \"confidence intervals to compute, which must be between 0% and 100% inclusive. units\",\n \"Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\":\",\n \"Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"bokeh. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key word\",\n \"\\\"mic-pk-pk\\\" kwargs : optional Additional key word arguments can be passed to change\",\n \"(3D). This function plots orbits for each node on the rotor system in\",\n \"\\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for j, p in enumerate(conf_interval):\",\n \"if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label\",\n \"node for each frequency. phase : array Phase of the frequency response for\",\n \"conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response. This method plots the time\",\n \"opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\",\",\n \"{i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude:\",\n \"{i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\",\",\n \"axis=0) p3 = np.percentile(self.yout[..., ndof * node + 1], 50 + p /\",\n \"an output and an input. Parameters ---------- percentile : list, optional Sequence of\",\n \"%{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p\",\n \"in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 =\",\n \"i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 +\",\n \"None: fig = go.Figure() color_p = 0 color_i = 0 for i, p\",\n \"= np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 +\",\n \"for each node for each frequency. frequency_range : array Array with the frequencies.\",\n \"pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile\",\n \"= self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items():\",\n \"i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2, axis=1)\",\n \"plot to check other speeds which also excite a specific natural frequency. Two\",\n \"plots orbits for each node on the rotor system in a 3D view.\",\n \"object with the plot. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\"\",\n \"np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j,\",\n \"title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\",\",\n \"== \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval\",\n \"analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np from plotly import express as\",\n \"mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1}\",\n \"pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" #\",\n \"operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) **\",\n \"observe the response. percentile : list, optional Sequence of percentiles to compute, which\",\n \"+ \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"key word arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"),\",\n \"conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None:\",\n \"in enumerate(self.phase): aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i +\",\n \"Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase\",\n \"system in a 3D view. Parameters ---------- percentile : list, optional Sequence of\",\n \"number_dof, nodes_list, nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list\",\n \"col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict( radialaxis=fig2.layout.polar.radialaxis,\",\n \"\\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for k, v in default_values.items():\",\n \"for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\",\",\n \"for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5,\",\n \"method plots the frequency response magnitude given an output and an input using\",\n \"speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec def\",\n \"The figure object with the plot. units : str, optional Unit system Default\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]),\",\n \"Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \") probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp =\",\n \"{}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" +\",\n \") for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean -\",\n \"on the rotor system in a 2D view. Parameters ---------- node : int\",\n \"= operator @ np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) **\",\n \"in default_values.items(): kwargs.setdefault(k, v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter(\",\n \"{i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) )\",\n \"{i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p +=\",\n \"p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) )\",\n \"1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1,\",\n \"+ \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"arguments can be passed to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...)\",\n \"response given an output and an input. Parameters ---------- percentile : list, optional\",\n \"fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j,\",\n \"p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof * node], 50 + p /\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof\",\n \"+ p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 -\",\n \"/ 2, axis=0) p2 = np.percentile(self.yout[..., ndof * node], 50 - p /\",\n \"axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} -\",\n \"= go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i +\",\n \"theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} -\",\n \"default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in\",\n \"array Phase of the frequency response for node for each frequency. number_dof =\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16),\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. args : optional\",\n \"frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof):\",\n \") ) color_p += 1 for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp,\",\n \"Plotly. Parameters ---------- dof : int Degree of freedom. units : str Magnitude\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for\",\n \"phase angle response. This method plots the phase response given an output and\",\n \"): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response given\",\n \"fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot( self,\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot\",\n \"in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 =\",\n \"angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i +\",\n \"fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y\",\n \"args: optional harmonics : list, optional List with the harmonics to be plotted.\",\n \"title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ),\",\n \"* node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[..., ndof *\",\n \"p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2 = np.percentile(probe_phase, 50\",\n \"= speed_range self.wd = wd self.log_dec = log_dec def plot_nat_freq(self, percentile=[], conf_interval=[], harmonics=[1],\",\n \"# fmt: on for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=1, col=1)\",\n \"decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200,\",\n \"p, axis=0), opacity=1.0, name=\\\"percentile: {}%\\\".format(p), line=dict(width=3, color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0\",\n \"* node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude:\",\n \"units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ---------- probe :\",\n \"fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1}\",\n \"the damped natural frequencies log_dec : array Array with the Logarithmic decrement Returns\",\n \"a tuple of probes with their nodes and orientations. Parameters ---------- probe :\",\n \"{p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1]))\",\n \"figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range,\",\n \"Plotly Python Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure()\",\n \"np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1),\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx\",\n \"array Array with the speed range in rad/s. magnitude : array Array with\",\n \"+ 1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" +\",\n \"units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots the frequency response\",\n \"provide plots for Time Response and Orbit Response. Parameters ---------- time_range : 1-dimensional\",\n \"None: fig = go.Figure() default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile)\",\n \"vs frequency. \\\"\\\"\\\" fig0 = self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for\",\n \"np.vstack((mag[:, dofx], mag[:, dofy])) probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1]\",\n \"as np from plotly import express as px from plotly import graph_objects as\",\n \"...) *See Plotly Python Figure Reference for more information. Returns ------- fig :\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(self.magnitude, p, axis=1), theta=np.percentile(self.phase, p, axis=1), customdata=self.speed_range,\",\n \"axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,\",\n \"class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Forced Response. Parameters ----------\",\n \"= np.percentile(self.magnitude, 50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 -\",\n \"y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p,\",\n \"axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True\",\n \"fig is None: fig = go.Figure() color_p = 0 color_i = 0 for\",\n \"fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"np.percentile(self.yout[..., ndof * node + 1], 50 + p / 2, axis=0) p4\",\n \"\\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ\",\n \"ndof * node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=(\",\n \"to change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python\",\n \"= time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list self.nodes_pos =\",\n \"graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label\",\n \"conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure()\",\n \"ndof * node + 1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter(\",\n \"fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1}\",\n \"v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile,\",\n \"fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\":\",\n \"This method plots the frequency and phase response given an output and an\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\")\",\n \"harmonics in a single plot to check other speeds which also excite a\",\n \"- X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults:\",\n \"unbalance response magnitude. Parameters ---------- probe : list of tuples List with tuples\",\n \"= np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x\",\n \"plot_type. The default is bokeh Parameters ---------- speed_range : array Array with the\",\n \"go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\"\",\n \"np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0])\",\n \"False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y -\",\n \"------- fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if\",\n \"color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe,\",\n \") return fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ):\",\n \"= 0 for i, p in enumerate(probe): dofx = p[0] * self.number_dof dofy\",\n \"the frequencies. magnitude : array Magnitude of the frequency response for node for\",\n \"= np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 -\",\n \"frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range = speed_range\",\n \"name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, )\",\n \"px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for\",\n \"probe_resp = np.zeros_like(self.magnitude[:, :, 0]) for j, mag in enumerate(self.magnitude): _probe_resp = operator\",\n \"the attribute plot_type. The default is bokeh Parameters ---------- speed_range : array Array\",\n \"name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes(\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. units : str\",\n \"conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2,\",\n \"%{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval):\",\n \": str, optional Unit system Default is \\\"mic-pk-pk\\\" kwargs : optional Additional key\",\n \"the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference for\",\n \"/ 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace(\",\n \"title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout(\",\n \"angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[],\",\n \"self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2,\",\n \"{}\\\".format(j + 1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, )\",\n \"be between 0 and 100 inclusive. units : str, optional Unit system Default\",\n \"**kwargs, ) ) for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof\",\n \"magnitude : array Magnitude of the frequency response for node for each frequency.\",\n \"/ 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])), theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\",\",\n \"color=colors1[i]), legendgroup=\\\"perc{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\"\",\n \"p in enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof\",\n \"ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots for Campbell Diagram. It's possible to\",\n \"input/output. phase : array Array with the frequencies, phase of the frequency response\",\n \"subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2, col=1) subplots.update_layout( plot_bgcolor=\\\"white\\\", polar_bgcolor=\\\"white\\\", width=1800,\",\n \"opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude:\",\n \"phase angle vs frequency. \\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval,\",\n \"i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i,\",\n \"frequencies log_dec : array Array with the Logarithmic decrement Returns ------- subplots :\",\n \"1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase:\",\n \"with the force response for each node for each frequency. frequency_range : array\",\n \"element's node. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with amplitude vs\",\n \"the phase response given an output and an input using bokeh. Parameters ----------\",\n \"rotor system in a 2D view. Parameters ---------- node : int Select the\",\n \"+ \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit:\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\",\",\n \"height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults:\",\n \"axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\",\",\n \"2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2)\",\n \"\\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile : list, optional\",\n \"range in rad/s. wd : array Array with the damped natural frequencies log_dec\",\n \"default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_phase(percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units,\",\n \"row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig0.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig1.layout.xaxis, row=2, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=2,\",\n \") x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude,\",\n \"nodes_pos: array Rotor nodes axial positions. number_dof : int Number of degrees of\",\n \"_probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] *\",\n \"- p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2,\",\n \"graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" def __init__(self,\",\n \": Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval)\",\n \"------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot. \\\"\\\"\\\" if\",\n \"x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency:\",\n \"fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1,\",\n \"for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:, dofx], y[:, dofy]))\",\n \"for j, mag in enumerate(self.magnitude): _probe_resp = operator @ np.vstack((mag[:, dofx], mag[:, dofy]))\",\n \"for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\",\",\n \"go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time:\",\n \"legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"the plot. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs :\",\n \"__init__(self, speed_range, wd, log_dec): self.speed_range = speed_range self.wd = wd self.log_dec = log_dec\",\n \"legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" +\",\n \"axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude:\",\n \"np.percentile(probe_phase, 50 + p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p\",\n \"\\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude =\",\n \"node + 1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X -\",\n \"{i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency:\",\n \"p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.magnitude, p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile:\",\n \"be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure() The figure object\",\n \"interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\",\",\n \"time_range : 1-dimensional array Time array. yout : array System response. xout :\",\n \"np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt:\",\n \"mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True,\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"+ 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, )\",\n \"**kwargs): \\\"\\\"\\\"Plot the damped natural frequencies vs frequency. Parameters ---------- percentile : list,\",\n \"), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def plot(self,\",\n \"axis=0) p2 = np.percentile(probe_phase, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x,\",\n \"conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters ----------\",\n \"speed_range self.magnitude = magnitude self.phase = phase def plot_magnitude( self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\",\",\n \"legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p\",\n \"phase response given an output and an input using bokeh. Parameters ---------- percentile\",\n \") for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data,\",\n \"%{y:.2e}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p,\",\n \"line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else\",\n \"response (2D). This function plots orbits for a given node on the rotor\",\n \"for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude,\",\n \"hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace(\",\n \"...) *See Plotly Python Figure Reference for more information. Returns ------- subplots :\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"p2 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1,\",\n \"enumerate(probe): dofx = p[0] * self.number_dof dofy = p[0] * self.number_dof + 1\",\n \"to check other speeds which also excite a specific natural frequency. Two options\",\n \"2, axis=0) p3 = np.percentile(probe_phase, 50 + p / 2, axis=0) p4 =\",\n \"fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None,\",\n \") ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p\",\n \"x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs,\",\n \") fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict( tickfont=dict(size=14), gridcolor=\\\"lightgray\\\",\",\n \"check other speeds which also excite a specific natural frequency. Two options for\",\n \"compute, which must be between 0 and 100 inclusive. fig : Plotly graph_objects.Figure()\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results\",\n \"fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i +\",\n \"\\\"\\\"\\\" # fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 =\",\n \"ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0), opacity=1.0, name=\\\"Mean\\\",\",\n \"name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs,\",\n \"= dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items():\",\n \"\\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j,\",\n \"/ 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1], 50 -\",\n \"p2 = np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3\",\n \"subplots class ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Frequency Response. Parameters\",\n \"line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\"\",\n \"= go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\",\",\n \"(m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label =\",\n \": Plotly graph_objects.Figure() The figure object with the plot. units : str, optional\",\n \"word arguments can be passed to change the plot layout only (e.g. width=1000,\",\n \"np.ones(self.yout.shape[1]) * self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof\",\n \"%{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j,\",\n \"interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs)\",\n \"probe_resp[i] = np.sqrt((_probe_resp[0] * np.cos(angle)) ** 2 + (_probe_resp[1] * np.sin(angle)) ** 2)\",\n \"{i + 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced\",\n \"enumerate(percentile): fig.add_trace( go.Scatterpolar( r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5,\",\n \"color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"),\",\n \"from plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme import\",\n \"change the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure\",\n \"* n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n\",\n \"= np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\"\",\n \"multiples harmonics in a single plot to check other speeds which also excite\",\n \"each frequency. number_dof = int Number of degrees of freedom per shaft element's\",\n \"graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values =\",\n \"set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults:\",\n \"probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots\",\n \"module. This module returns graphs for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\"\",\n \"given a set of probes. Parameters ---------- dof : int Degree of freedom\",\n \"plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots\",\n \"1], 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])),\",\n \"thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i +\",\n \"orbit response. percentile : list, optional Sequence of percentiles to compute, which must\",\n \"**kwargs): \\\"\\\"\\\"Plot orbit response (2D). This function plots orbits for a given node\",\n \"fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}%\",\n \"for i, p in enumerate(conf_interval): fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50\",\n \"conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the unbalance\",\n \"- Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)),\",\n \"given an output and an input using bokeh. Parameters ---------- percentile : list,\",\n \"ndof * n + 1], 50 - p / 2, axis=0 ), line=dict(width=3.5,\",\n \"p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i +\",\n \"fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Forced Response. Parameters\",\n \"fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe {i\",\n \"frequency_range, number_dof): self.forced_resp = forced_resp self.magnitude = magnitude self.phase = phase self.frequency_range =\",\n \"intervals to compute, which must be between 0 and 100 inclusive. args: optional\",\n \"if fig is None: fig = go.Figure() color_i = 0 color_p = 0\",\n \"conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v)\",\n \"plots orbits for a given node on the rotor system in a 2D\",\n \"%{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for i, p\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.time_range, self.time_range[::-1])) for\",\n \"opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude:\",\n \"name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\",\",\n \"p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, )\",\n \"+ 1} - confidence interval: {p}%\\\", ) ) color_i += 1 fig.update_layout( polar=dict(\",\n \"%{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) )\",\n \"y=np.mean(self.phase, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs,\",\n \"theta=np.concatenate((p3, p4[::-1])), thetaunit=\\\"radians\\\", line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs,\",\n \"information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with magnitude plot.\",\n \"per shaft element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object\",\n \"\\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval =\",\n \"the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if\",\n \"where the probe is located. orientation : float, probe orientation angle about the\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\",\",\n \"**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 +\",\n \"for each node on the rotor system in a 3D view. Parameters ----------\",\n \"fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p),\",\n \"an input using Plotly. Parameters ---------- percentile : list, optional Sequence of percentiles\",\n \"x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i + 1} - confidence\",\n \"\\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label =\",\n \"tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( width=1200, height=900, plot_bgcolor=\\\"white\\\", legend=dict( font=dict(family=\\\"sans-serif\\\",\",\n \"speed range in rad/s. magnitude : array Array with the frequencies, magnitude (dB)\",\n \"(e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more information. Returns\",\n \": optional Additional key word arguments can be passed to change the plot\",\n \"legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" +\",\n \"interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time (s)</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\")\",\n \"- Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace(\",\n \"hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig\",\n \"p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i],\",\n \"2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 + p /\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots\",\n \"layout (e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more information.\",\n \"response using Plotly. Parameters ---------- dof : int Degree of freedom. units :\",\n \"1), line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs, ) ) for\",\n \"the plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units\",\n \"= np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in enumerate(self.phase): aux_phase = phs[:,\",\n \"self.yout[..., ndof * n + 1], 50 - p / 2, axis=0 ),\",\n \"self.frequency_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p /\",\n \"line=dict(width=3, color=colors1[j]), legendgroup=\\\"mean{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Log Dec: %{y:.3f}\\\"), **kwargs, ) ) for\",\n \"y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof * n + 1], axis=0), line=dict(width=5,\",\n \"be between 0 and 100 inclusive. args: optional harmonics : list, optional List\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j,\",\n \"opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"), **kwargs,\",\n \"2}], [{}, None]] ) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data\",\n \"Two options for plooting are available: Matplotlib and Bokeh. The user chooses between\",\n \"angle). node : int indicate the node where the probe is located. orientation\",\n \"color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} -\",\n \"the plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference\",\n \"amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase):\",\n \"**kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"*args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1)\",\n \"+ \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) for j, p in\",\n \"fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict(\",\n \"np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter(\",\n \"%{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_phase,\",\n \"= ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] * np.sin(angle) ** 2\",\n \"* np.pi if i < 0 else i for i in aux_phase] )\",\n \"for frequency and log dec. \\\"\\\"\\\" def __init__(self, speed_range, wd, log_dec): self.speed_range =\",\n \"__init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase\",\n \"y=np.percentile(probe_phase, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile:\",\n \"50 + p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p /\",\n \") fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 = np.percentile(probe_resp, 50 + p\",\n \"+ \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in\",\n \"np.percentile(probe_resp, 50 - p / 2, axis=0) p3 = np.percentile(probe_phase, 50 + p\",\n \"fmt: off fig0 = self.plot_magnitude(probe, percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile,\",\n \"- Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) for j, p in enumerate(percentile): fig.add_trace(\",\n \"p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 = np.percentile(self.wd[j], 50\",\n \"Response. Parameters ---------- force_resp : array Array with the force response for each\",\n \"np.sort(percentile) if units == \\\"m\\\": r_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\":\",\n \"legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1]))\",\n \"p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p / 2, axis=1)\",\n \"Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout( plot_bgcolor=\\\"white\\\", width=1200,\",\n \"plots the unbalance response magnitude. Parameters ---------- probe : list of tuples List\",\n \"np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @ np.vstack((y[:,\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for\",\n \"axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" +\",\n \"def plot_1d( self, probe, percentile=[], conf_interval=[], fig=None, *args, **kwargs ): \\\"\\\"\\\"Plot time response.\",\n \"p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\",\",\n \"1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_p += 1 x\",\n \"the response. percentile : list, optional Sequence of percentiles to compute, which must\",\n \"): \\\"\\\"\\\"Plot amplitude vs frequency. This method plots the frequency response magnitude given\",\n \"go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[..., ndof * node + 1], axis=0),\",\n \"2, axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p /\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.phase,\",\n \"speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude self.phase = phase def\",\n \"fig = go.Figure() line = np.zeros(len(self.nodes_pos)) fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\",\",\n \"go from plotly import io as pio from plotly.subplots import make_subplots from ross.plotly_theme\",\n \"(e.g. width=800, height=600, ...). *See Plotly Python Figure Reference for more information. Returns\",\n \"ST_FrequencyResponseResults: \\\"\\\"\\\"Store stochastic results and provide plots for Frequency Response. Parameters ---------- speed_range\",\n \"fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False),\",\n \"size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return subplots class ST_TimeResponseResults: \\\"\\\"\\\"Store stochastic results\",\n \"plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units\",\n \"the state vector. nodes_list: array list with nodes from a rotor model. nodes_pos:\",\n \"i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof * node], p, axis=0), y=np.percentile(self.yout[...,\",\n \"possible to visualize multiples harmonics in a single plot to check other speeds\",\n \"output and an input using bokeh. Parameters ---------- percentile : list, optional Sequence\",\n \"opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" +\",\n \"graphs for each type of analyses in st_rotor_assembly.py. \\\"\\\"\\\" import numpy as np\",\n \"p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range,\",\n \"to visualize multiples harmonics in a single plot to check other speeds which\",\n \"change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference\",\n \"n + 1], 50 + p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6,\",\n \"Plotly graph_objects.Figure() The figure object with the plot. units : str Magnitude unit\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\",\",\n \"return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response\",\n \"axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency:\",\n \"from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\" # set Plotly palette of colors\",\n \"+ 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Rotor speed</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \": array Array with the frequencies. magnitude : array Magnitude of the frequency\",\n \"None]] ) # fmt: on for data in fig0[\\\"data\\\"]: data.showlegend = False fig.add_trace(data,\",\n \"+ 1} - Mean\\\", line=dict(width=3.0), hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"of degrees of freedom per shaft element's node. Returns ------- subplots : Plotly\",\n \"dofx], y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1]\",\n \"\\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof\",\n \"= self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if\",\n \"== \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" for\",\n \"range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0, name=\\\"Mean - Mode {}\\\".format(j + 1),\",\n \"given a tuple of probes with their nodes and orientations. Parameters ---------- probe\",\n \"bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot_phase(self, percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase\",\n \"\\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig = go.Figure() color_p = 0 color_i\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True,\",\n \"tuples List with tuples (node, orientation angle). node : int indicate the node\",\n \"/ 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3,\",\n \"percentile=[], conf_interval=[], **kwargs): \\\"\\\"\\\"Plot phase angle response. This method plots the phase response\",\n \"of the frequency response for each pair input/output. Returns ------- subplots : Plotly\",\n \"%{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"%{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in enumerate(conf_interval):\",\n \"Campbell Diagram. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"0 refers to +X direction. percentile : list, optional Sequence of percentiles to\",\n \"thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", legendgroup=f\\\"Probe {i\",\n \"axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x,\",\n \"**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.yout[..., ndof *\",\n \"1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ),\",\n \"- percentile: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\",\",\n \"a 3D view. Parameters ---------- percentile : list, optional Sequence of percentiles to\",\n \"1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1,\",\n \"int Degree of freedom. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\"\",\n \"\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i, p in\",\n \"response magnitude given an output and an input using Plotly. Parameters ---------- percentile\",\n \"plots the time response given a tuple of probes with their nodes and\",\n \") ) x = np.concatenate((self.time_range, self.time_range[::-1])) for j, p in enumerate(conf_interval): p1 =\",\n \"axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False, hovertemplate=(\",\n \"the plot. \\\"\\\"\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for\",\n \"v) if fig is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof *\",\n \"the plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None:\",\n \": array Phase of the frequency response for node for each frequency. number_dof\",\n \"size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20),\",\n \"np.percentile(self.yout[..., ndof * node], 50 + p / 2, axis=0) p2 = np.percentile(self.yout[...,\",\n \"1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Phase: %{y:.2f}\\\", )\",\n \"self.nodes_list = nodes_list self.nodes_pos = nodes_pos self.number_dof = number_dof def plot_1d( self, probe,\",\n \"for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50 + p / 2,\",\n \"element's node Returns ------- fig : Plotly graph_objects.Figure() The figure object with the\",\n \"j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs,\",\n \"go.Scatter( x=self.speed_range, y=np.percentile(self.wd[j], p, axis=1), opacity=0.6, line=dict(width=2.5, color=colors2[j]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}{}\\\".format(j, i), hovertemplate=(\",\n \"angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\", line=dict(width=3.0, color=list(tableau_colors)[i]), name=f\\\"Probe\",\n \"2 ) # fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i\",\n \"= px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results and provide plots\",\n \"**kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 +\",\n \"p / 2, axis=0) p4 = np.percentile(probe_phase, 50 - p / 2, axis=0)\",\n \"\\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig\",\n \"= \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig = go.Figure() color_p = 0\",\n \"more information. Returns ------- fig : Plotly graph_objects.Figure() The figure object with the\",\n \"np.sort(percentile) if fig is None: fig = go.Figure() color_i = 0 color_p =\",\n \"conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the frequency\",\n \"results and provide plots for Campbell Diagram. It's possible to visualize multiples harmonics\",\n \"p, axis=0), opacity=0.6, line=dict(width=2.5), name=f\\\"Probe {i + 1} - percentile: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\"\",\n \"k, v in default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1]))\",\n \"node to display the respective orbit response. percentile : list, optional Sequence of\",\n \"frequency response for node for each frequency. number_dof = int Number of degrees\",\n \"The figure object with the plot. \\\"\\\"\\\" if fig is None: fig =\",\n \"percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is\",\n \"showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=y_axis_label, title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"r=np.percentile(probe_resp, p, axis=0), theta=np.percentile(probe_phase, p, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", opacity=0.6, line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i\",\n \"plots the frequency and phase response given an output and an input. Parameters\",\n \"= go.Figure() color_p = 0 color_i = 0 for i, p in enumerate(probe):\",\n \"Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\"\",\n \"orientation angle). node : int indicate the node where the probe is located.\",\n \"graph_objects.Figure() The figure object with the plot. kwargs : optional Additional key word\",\n \"name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\", hovertemplate=(\",\n \"percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and\",\n \"optional Sequence of percentiles to compute, which must be between 0 and 100\",\n \"= np.sort(percentile) if fig is None: fig = go.Figure() color_p = 0 color_i\",\n \"= probe_phase[i] - angle fig.add_trace( go.Scatterpolar( r=np.mean(probe_resp, axis=0), theta=np.mean(probe_phase, axis=0), customdata=self.frequency_range, thetaunit=\\\"radians\\\", mode=\\\"lines\\\",\",\n \"2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3 =\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) for j, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"phase response given a set of probes. Parameters ---------- probe : list of\",\n \"orbits for a given node on the rotor system in a 2D view.\",\n \"aux_phase = phs[:, p[0] * self.number_dof] probe_phase[i] = np.array( [i + 2 *\",\n \"an input. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"Number of degrees of freedom per shaft element's node. Returns ------- subplots :\",\n \"Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" fig0\",\n \"plot. units : str, optional Unit system Default is \\\"mic-pk-pk\\\". kwargs : optional\",\n \"1} - confidence interval: {p}%\\\", legendgroup=f\\\"Probe {i + 1} - confidence interval: {p}%\\\",\",\n \"inclusive. fig : Plotly graph_objects.Figure() The figure object with the plot. args :\",\n \"fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0),\",\n \"as pio from plotly.subplots import make_subplots from ross.plotly_theme import tableau_colors pio.renderers.default = \\\"browser\\\"\",\n \"2 + (_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range,\",\n \"tickfont=dict(size=14), gridcolor=\\\"lightgray\\\", linecolor=\\\"black\\\", linewidth=2.5, ), ), ) return fig def plot(self, percentile=[], conf_interval=[],\",\n \"interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal Position:\",\n \"legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1]))\",\n \"- Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) for\",\n \"fig.add_trace( go.Scatter3d( x=self.nodes_pos, y=line, z=line, line=dict(width=2.0, color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for\",\n \"polar forced response using Plotly. Parameters ---------- dof : int Degree of freedom.\",\n \"X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store\",\n \"in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1,\",\n \"to observe the response. percentile : list, optional Sequence of percentiles to compute,\",\n \": Plotly graph_objects.Figure() The figure object with the plot. args : optional Additional\",\n \"+ \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p\",\n \"= dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v) fig1 = self.plot_log_dec(percentile, conf_interval,\",\n \"v in default_values.items(): kwargs.setdefault(k, v) for i, p in enumerate(probe): dofx = p[0]\",\n \"layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for more\",\n \"float, probe orientation angle about the shaft. The 0 refers to +X direction.\",\n \"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") fig.update_layout(title=\\\"<b>Rotor Orbit: node {}</b>\\\".format(node)), return fig def\",\n \"50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 - p /\",\n \"compute, which must be between 0 and 100 inclusive. units : str, optional\",\n \"array Time evolution of the state vector. nodes_list: array list with nodes from\",\n \"direction. percentile : list, optional Sequence of percentiles to compute, which must be\",\n \"- p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]),\",\n \"plot_magnitude( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This\",\n \"line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False, hovertemplate=( \\\"Nodal\",\n \"Additional plot axes kwargs : optional Additional key word arguments can be passed\",\n \": array System response. xout : array Time evolution of the state vector.\",\n \"showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False), zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig\",\n \"Response. Parameters ---------- time_range : 1-dimensional array Time array. yout : array System\",\n \"pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is None: fig = go.Figure()\",\n \"confidence intervals to compute, which must be between 0 and 100 inclusive. units\",\n \"graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" fig0\",\n \"list, optional List withe the harmonics to be plotted. The default is to\",\n \"frequency and log dec. \\\"\\\"\\\" fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values =\",\n \"subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14),\",\n \"Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hovertemplate=( \\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\"\",\n \"Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ),\",\n \"optional Additional key word arguments can be passed to change the plot layout\",\n \"j, p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0)\",\n \"y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if fig is\",\n \"angle = p[1] # fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle),\",\n \"r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig is\",\n \"in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50 + p / 2, axis=1) p2 =\",\n \"= np.percentile(self.phase, 50 + p / 2, axis=1) p2 = np.percentile(self.phase, 50 -\",\n \"them using the attribute plot_type. The default is bokeh Parameters ---------- speed_range :\",\n \"cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data,\",\n \": 1-dimensional array Time array. yout : array System response. xout : array\",\n \"np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2, axis=0) fig.add_trace(\",\n \"= np.percentile(self.yout[..., ndof * node + 1], 50 - p / 2, axis=0)\",\n \"+ p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2,\",\n \"node {}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit\",\n \"p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\",\",\n \"to display the respective orbit response. percentile : list, optional Sequence of percentiles\",\n \"the shaft. The 0 refers to +X direction. percentile : list, optional Sequence\",\n \"self.plot_polar_bode(probe, percentile, conf_interval, units=units, **kwargs) if fig is None: fig = make_subplots( rows=2,\",\n \"1, 1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis,\",\n \"nodes_pos): self.time_range = time_range self.yout = yout self.xout = xout self.nodes_list = nodes_list\",\n \"= self.plot_magnitude(percentile, conf_interval, units, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items():\",\n \"in fig1[\\\"data\\\"]: data.showlegend = False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data,\",\n \"kwargs : optional Additional key word arguments can be passed to change the\",\n \"v) fig = go.Figure() fig.add_trace( go.Scatterpolar( r=np.mean(self.magnitude, axis=1), theta=np.mean(self.phase, axis=1), customdata=self.speed_range, thetaunit=\\\"radians\\\", line=dict(width=3.0,\",\n \"if j == 0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X -\",\n \"row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2, col=1) fig.update_layout( polar=dict(\",\n \"y[:, dofy])) probe_resp[j] = ( _probe_resp[0] * np.cos(angle) ** 2 + _probe_resp[1] *\",\n \": list, optional Sequence of percentiles to compute, which must be between 0\",\n \"fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_layout(\",\n \") ) fig.update_xaxes( title_text=\\\"<b>Frequency</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, )\",\n \"(2D). This function plots orbits for a given node on the rotor system\",\n \"plot layout only (e.g. width=1000, height=800, ...). *See Plotly Python Figure Reference for\",\n \"px from plotly import graph_objects as go from plotly import io as pio\",\n \"opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude:\",\n \"fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig def plot_polar_bode( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\",\",\n \"fig.update_yaxes(title_text=\\\"<b>Amplitude</b>\\\") return fig def plot_2d(self, node, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit\",\n \"ndof * node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[..., ndof\",\n \"for i, p in enumerate(probe): probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs\",\n \"is None: fig = go.Figure() fig.add_trace( go.Scatter( x=np.mean(self.yout[..., ndof * node], axis=0), y=np.mean(self.yout[...,\",\n \"np.percentile(self.yout[..., ndof * node], 50 - p / 2, axis=0) p3 = np.percentile(self.yout[...,\",\n \"array Array with the damped natural frequencies log_dec : array Array with the\",\n \"Array with the speed range in rad/s. magnitude : array Array with the\",\n \"= np.zeros_like(self.yout[:, :, 0]) for j, y in enumerate(self.yout): _probe_resp = operator @\",\n \"The default is bokeh Parameters ---------- speed_range : array Array with the speed\",\n \"Response and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout\",\n \"information. Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency\",\n \"else i for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i]\",\n \"a given node on the rotor system in a 2D view. Parameters ----------\",\n \"linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Phase Angle</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"to plot 1x. kwargs : optional Additional key word arguments can be passed\",\n \"- confidence interval: {p}%\\\", hovertemplate=(\\\"Time: %{x:.3f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Time\",\n \"np.percentile(probe_resp, 50 - p / 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\",\",\n \"n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1], p, axis=0), opacity=1.0, name=\\\"percentile:\",\n \"given an output and an input. Parameters ---------- percentile : list, optional Sequence\",\n \"array Array with the frequencies. magnitude : array Magnitude of the frequency response\",\n \"= make_subplots(rows=1, cols=2) for data in fig0[\\\"data\\\"]: subplots.add_trace(data, 1, 1) for data in\",\n \"Parameters ---------- speed_range : array Array with the speed range in rad/s. magnitude\",\n \"is \\\"mic-pk-pk\\\" polar_kwargs : optional Additional key word arguments can be passed to\",\n \"fig = go.Figure() color_i = 0 color_p = 0 for i, p in\",\n \"thetaunit=\\\"radians\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i], opacity=0.5, name=f\\\"Probe {i + 1} - confidence interval:\",\n \"excite a specific natural frequency. Two options for plooting are available: Matplotlib and\",\n \"0% and 100% inclusive. units : str, optional Unit system Default is \\\"mic-pk-pk\\\".\",\n \"and phase response given an output and an input. Parameters ---------- percentile :\",\n \"fig0 = self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in\",\n \"%{y:.3f}\\\"), **kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1),\",\n \"other speeds which also excite a specific natural frequency. Two options for plooting\",\n \"x=self.frequency_range, y=np.mean(probe_phase, axis=0), opacity=1.0, mode=\\\"lines\\\", line=dict(width=3, color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\",\",\n \"Parameters ---------- probe : list of tuples List with tuples (node, orientation angle).\",\n \"view. Parameters ---------- percentile : list, optional Sequence of percentiles to compute, which\",\n \"thetaunit=\\\"radians\\\", line=dict(width=3.0, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency:\",\n \"%{y:.2f}\\\", ) ) color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p\",\n \"v) fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"),\",\n \"be passed to change the plot layout only (e.g. width=1000, height=800, ...). *See\",\n \"fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} - Mean\\\", line=dict(width=3.0),\",\n \"np.sin(angle)) ** 2) # fmt: on fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_resp, axis=0), opacity=1.0, mode=\\\"lines\\\",\",\n \"%{x:.3f}<br>\\\" + \\\"Frequency: %{y:.3f}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval):\",\n \"in fig1[\\\"data\\\"]: subplots.add_trace(data, 1, 2) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1,\",\n \"gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5, linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Logarithmic decrement</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\",\",\n \"enumerate(percentile): fig.add_trace( go.Scatter( x=self.frequency_range, y=np.percentile(probe_resp, p, axis=0), opacity=0.6, mode=\\\"lines\\\", line=dict(width=2.5, color=colors1[color_p]), name=f\\\"Probe {i\",\n \"1], p, axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\"\",\n \"tuples (node, orientation angle). node : int indicate the node where the probe\",\n \"{}</b>\\\".format(node)), return fig def plot_3d(self, percentile=[], conf_interval=[], fig=None, *args, **kwargs): \\\"\\\"\\\"Plot orbit response\",\n \"np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp = np.zeros_like(self.yout[:, :, 0]) for j, y\",\n \"x=x, y=np.percentile(self.yout[..., ndof * n], p, axis=0), z=np.percentile(self.yout[..., ndof * n + 1],\",\n \"angle vs frequency. \\\"\\\"\\\" def __init__(self, forced_resp, magnitude, phase, frequency_range, number_dof): self.forced_resp =\",\n \"legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, )\",\n \"Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i,\",\n \"**kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=np.percentile(self.yout[..., ndof *\",\n \"(_probe_resp[1] * np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0])\",\n \"p in enumerate(conf_interval): p1 = np.percentile(probe_phase, 50 + p / 2, axis=0) p2\",\n \"), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.magnitude, 50\",\n \"axis=1) p3 = np.percentile(self.phase, 50 + p / 2, axis=1) p4 = np.percentile(self.phase,\",\n \"%{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs,\",\n \"fig=None, **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the phase response given a\",\n \"* node + 1], 50 + p / 2, axis=0) p4 = np.percentile(self.yout[...,\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) if fig is None: fig = go.Figure() color_p =\",\n \"legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def plot(self, percentile=[],\",\n \"= False fig.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: fig.add_trace(data, row=1, col=2) fig.update_xaxes(fig0.layout.xaxis,\",\n \"\\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p in\",\n \"color_i += 1 fig.update_layout( polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig\",\n \"plot. kwargs : optional Additional key word arguments can be passed to change\",\n \"plot. \\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units ==\",\n \"**kwargs, ) ) fig.update_layout( polar=dict( radialaxis=dict( title_text=r_axis_label, title_font=dict(family=\\\"Arial\\\", size=14), gridcolor=\\\"lightgray\\\", exponentformat=\\\"power\\\", ), angularaxis=dict(\",\n \"1} - Mean\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ),\",\n \"row=1, col=2) fig.update_xaxes(fig0.layout.xaxis, row=1, col=1) fig.update_yaxes(fig0.layout.yaxis, row=1, col=1) fig.update_xaxes(fig1.layout.xaxis, row=2, col=1) fig.update_yaxes(fig1.layout.yaxis, row=2,\",\n \"axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe {i +\",\n \"conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase\",\n \"**kwargs, ) ) for j in range(self.wd.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.wd[j], axis=1), opacity=1.0,\",\n \"color=\\\"black\\\", dash=\\\"dashdot\\\"), showlegend=False, mode=\\\"lines\\\", ) ) for j, n in enumerate(self.nodes_list): x =\",\n \"response. This method plots the frequency and phase response given a set of\",\n \") return fig def plot(self, percentile=[], conf_interval=[], *args, **kwargs): \\\"\\\"\\\"Plot Campbell Diagram. This\",\n \"1], axis=0), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y\",\n \"Dec: %{y:.3f}\\\"), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range,\",\n \"percentile, conf_interval, units=units, **kwargs) fig1 = self.plot_phase(probe, percentile, conf_interval, **kwargs) fig2 = self.plot_polar_bode(probe,\",\n \"phase : array Array with the frequencies, phase of the frequency response for\",\n \"* self.number_dof + 1 angle = p[1] # fmt: off operator = np.array(\",\n \"legendgroup=\\\"conf_interval{}\\\".format(p), showlegend=False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" + \\\"Y\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" if fig\",\n \"fig = go.Figure() fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.magnitude, axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\",\",\n \"\\\"\\\"\\\" if units == \\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\":\",\n \"to compute, which must be between 0 and 100 inclusive. conf_interval : list,\",\n \"row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\",\",\n \"dof : int Degree of freedom. units : str Magnitude unit system. Default\",\n \") angle = p[1] probe_phase[i] = probe_phase[i] - angle fig.add_trace( go.Scatter( x=self.frequency_range, y=np.mean(probe_phase,\",\n \"graph_objects.make_subplots() Plotly figure with diagrams for frequency and log dec. \\\"\\\"\\\" fig0 =\",\n \"phase of the frequency response for each pair input/output. Returns ------- subplots :\",\n \"Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" ndof = self.number_dof default_values\",\n \"of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24 class ST_CampbellResults: \\\"\\\"\\\"Store stochastic results\",\n \"for i in aux_phase] ) angle = p[1] probe_phase[i] = probe_phase[i] - angle\",\n \"for Campbell Diagram. It's possible to visualize multiples harmonics in a single plot\",\n \"width=1200, height=900, legend=dict( font=dict(family=\\\"sans-serif\\\", size=14), bgcolor=\\\"white\\\", bordercolor=\\\"black\\\", borderwidth=2, ), ) return fig def\",\n \"Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter(\",\n \"* self.nodes_pos[n] fig.add_trace( go.Scatter3d( x=x, y=np.mean(self.yout[..., ndof * n], axis=0), z=np.mean(self.yout[..., ndof *\",\n \"color=list(tableau_colors)[i]), name=f\\\"Probe {i + 1} - Mean\\\", legendgroup=f\\\"Probe {i + 1} - Mean\\\",\",\n \"hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for\",\n \"- Mode {}\\\".format(p, j + 1), legendgroup=\\\"conf{}{}\\\".format(j, i), hoverinfo=\\\"none\\\", **kwargs, ) ) fig.update_xaxes(\",\n \"response given an output and an input using bokeh. Parameters ---------- percentile :\",\n \"---------- probe : list of tuples List with tuples (node, orientation angle). node\",\n \"harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency. Parameters ---------- percentile : list, optional\",\n \"), **kwargs, ) ) fig.update_layout( scene=dict( xaxis=dict(title=dict(text=\\\"<b>Rotor Length</b>\\\"), showspikes=False), yaxis=dict(title=dict(text=\\\"<b>Amplitude - X</b>\\\"), showspikes=False),\",\n \"+ \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), ) ) color_p += 1 for j, p in\",\n \"of percentiles to compute, which must be between 0 and 100 inclusive. conf_interval\",\n \"specific natural frequency. Two options for plooting are available: Matplotlib and Bokeh. The\",\n \"and Orbit Response. Parameters ---------- time_range : 1-dimensional array Time array. yout :\",\n \"This method plots the phase response given a set of probes. Parameters ----------\",\n \"col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\", width=1800,\",\n \"), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 = np.percentile(self.wd[j], 50\",\n \"percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot polar forced response using Plotly. Parameters\",\n \"axis=0), z=np.percentile( self.yout[..., ndof * n + 1], 50 - p / 2,\",\n \"x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j, h in enumerate(harmonics): fig.add_trace( go.Scatter( x=self.speed_range, y=self.speed_range\",\n \"%{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=y_axis_label) fig.update_layout(**kwargs) return fig def\",\n \"fig : Plotly graph_objects.Figure() The figure object with the plot. \\\"\\\"\\\" ndof =\",\n \"fmt: off operator = np.array( [[np.cos(angle), - np.sin(angle)], [np.cos(angle), + np.sin(angle)]] ) probe_resp\",\n \"fmt: on fig.add_trace( go.Scatter( x=self.time_range, y=np.mean(probe_resp, axis=0), opacity=1.0, name=f\\\"Probe {i + 1} -\",\n \"n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof * n +\",\n \"dofy = p[0] * self.number_dof + 1 angle = p[1] # fmt: off\",\n \"to compute, which must be between 0 and 100 inclusive. harmonics: list, optional\",\n \"is None: fig = make_subplots( rows=2, cols=2, specs=[[{}, {\\\"type\\\": \\\"polar\\\", \\\"rowspan\\\": 2}], [{},\",\n \"p4 = np.percentile(self.phase, 50 - p / 2, axis=1) fig.add_trace( go.Scatterpolar( r=np.concatenate((p1, p2[::-1])),\",\n \"conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k, v)\",\n \"\\\"browser\\\" # set Plotly palette of colors colors1 = px.colors.qualitative.Dark24 colors2 = px.colors.qualitative.Light24\",\n \"{}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range,\",\n \"color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), **kwargs, ) ) fig.update_layout( polar=dict(\",\n \"of the frequency response for node for each frequency. number_dof = int Number\",\n \"p / 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1)\",\n \"\\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label\",\n \"frequency. \\\"\\\"\\\" def __init__(self, speed_range, magnitude, phase): self.speed_range = speed_range self.magnitude = magnitude\",\n \"== \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values\",\n \"row=1, col=1) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=1) subplots.update_xaxes(fig0.layout.xaxis, row=1, col=2) subplots.update_yaxes(fig1.layout.yaxis, row=1, col=2) subplots.update_layout( plot_bgcolor=\\\"white\\\",\",\n \"Time evolution of the state vector. nodes_list: array list with nodes from a\",\n \"/ 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.5, name=f\\\"Probe\",\n \"/ 2, axis=1) p2 = np.percentile(self.magnitude, 50 - p / 2, axis=1) p3\",\n \"= p[0] * self.number_dof dofy = p[0] * self.number_dof + 1 angle =\",\n \"to change the plot (e.g. line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure\",\n \"line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), legendgroup=\\\"percentile{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, ) )\",\n \"list of tuples List with tuples (node, orientation angle). node : int indicate\",\n \"+ 1} - confidence interval: {p}%\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) color_i +=\",\n \"plot. \\\"\\\"\\\" conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) if units == \\\"m\\\": r_axis_label\",\n \"+ \\\"Phase: %{y:.2f}\\\"), **kwargs, ) ) x = np.concatenate((self.speed_range, self.speed_range[::-1])) for i, p\",\n \"This method plots the unbalance response magnitude. Parameters ---------- probe : list of\",\n \"for data in fig0[\\\"data\\\"]: subplots.add_trace(data, row=1, col=1) for data in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2,\",\n \"\\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\" + \\\"<b>Frequency: %{customdata:.2f}</b>\\\" ), **kwargs, ) ) for\",\n \"self.plot_nat_freq(percentile, conf_interval, *args, **kwargs) default_values = dict(showlegend=False) for k, v in default_values.items(): kwargs.setdefault(k,\",\n \"**kwargs) fig2 = self.plot_polar_bode(percentile, conf_interval, units, **kwargs) subplots = make_subplots( rows=2, cols=2, specs=[[{},\",\n \"%{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[..., ndof * n], 50\",\n \"polar=dict( radialaxis=dict(title_text=r_axis_label, exponentformat=\\\"E\\\"), angularaxis=dict(exponentformat=\\\"E\\\"), ), **kwargs, ) return fig def plot( self, probe,\",\n \"borderwidth=2, ), ) return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the\",\n \"in fig1[\\\"data\\\"]: subplots.add_trace(data, row=2, col=1) for data in fig2[\\\"data\\\"]: subplots.add_trace(data, row=1, col=2) subplots.update_xaxes(fig0.layout.xaxis,\",\n \"frequency. phase : array Phase of the frequency response for node for each\",\n \"line=dict(width=4.0, color=\\\"royalblue\\\"), opacity=1.0, ...) *See Plotly Python Figure Reference for more information. Returns\",\n \"in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p / 2, axis=1) p2 =\",\n \"opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Phase: %{y:.2f}\\\"), **kwargs, ) )\",\n \"fig def plot( self, probe, percentile=[], conf_interval=[], fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency\",\n \"= np.percentile(self.wd[j], 50 - p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])),\",\n \"stochastic results and provide plots for Forced Response. Parameters ---------- force_resp : array\",\n \"np from plotly import express as px from plotly import graph_objects as go\",\n \"axis=1), opacity=1.0, name=\\\"Mean\\\", line=dict(width=3, color=\\\"black\\\"), legendgroup=\\\"mean\\\", hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" + \\\"Amplitude: %{y:.2e}\\\"), **kwargs, )\",\n \"for a given node on the rotor system in a 2D view. Parameters\",\n \"- p / 2, axis=1) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\",\",\n \"Response. Parameters ---------- speed_range : array Array with the speed range in rad/s.\",\n \"np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v) fig =\",\n \"%{x:.2f}<br>Phase: %{y:.2f}\\\", ) ) color_i += 1 fig.update_xaxes(title_text=\\\"<b>Frequency</b>\\\") fig.update_yaxes(title_text=\\\"<b>Phase Angle</b>\\\") fig.update_layout(**kwargs), return fig\",\n \"plots for Campbell Diagram. It's possible to visualize multiples harmonics in a single\",\n \"1} - Mean\\\", hovertemplate=\\\"Frequency: %{x:.2f}<br>Amplitude: %{y:.2e}\\\", ) ) for j, p in enumerate(percentile):\",\n \"\\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments can be passed to change\",\n \"2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for j, phs in\",\n \"system Default is \\\"mic-pk-pk\\\". kwargs : optional Additional key word arguments can be\",\n \"a rotor model. nodes_pos: array Rotor nodes axial positions. number_dof : int Number\",\n \"else: y_axis_label = \\\"<b>Amplitude (dB)</b>\\\" default_values = dict(mode=\\\"lines\\\") conf_interval = np.sort(conf_interval) percentile =\",\n \"- Amplitude: %{x:.2e}<br>\\\" + \\\"Y - Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) fig.update_xaxes(title_text=\\\"<b>Amplitude</b>\\\")\",\n \"= np.concatenate((self.speed_range, self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0,\",\n \"damped natural frequencies log_dec : array Array with the Logarithmic decrement Returns -------\",\n \"p2[::-1])), line=dict(width=1, color=colors1[i]), fill=\\\"toself\\\", fillcolor=colors1[i], opacity=0.5, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf{}\\\".format(i), hovertemplate=(\\\"Frequency: %{x:.2f}<br>\\\" +\",\n \"{p}%\\\", legendgroup=f\\\"Probe {i + 1} - percentile{p}\\\", hovertemplate=( \\\"<b>Amplitude: %{r:.2e}</b><br>\\\" + \\\"<b>Phase: %{theta:.2f}</b><br>\\\"\",\n \"\\\"m\\\": y_axis_label = \\\"<b>Amplitude (m)</b>\\\" elif units == \\\"mic-pk-pk\\\": y_axis_label = \\\"<b>Amplitude (μ\",\n \"**kwargs): \\\"\\\"\\\"Plot frequency response. This method plots the frequency and phase response given\",\n \"and provide plots for Frequency Response. Parameters ---------- speed_range : array Array with\",\n \"- p / 2, axis=0) fig.add_trace( go.Scatter( x=np.concatenate((p1, p2[::-1])), y=np.concatenate((p3, p4[::-1])), line=dict(width=1, color=colors1[i]),\",\n \"* np.sin(angle)) ** 2) # fmt: on probe_phase = np.zeros_like(self.phase[:, :, 0]) for\",\n \"0 else False, hovertemplate=( \\\"Nodal Position: %{x:.2f}<br>\\\" + \\\"X - Amplitude: %{y:.2e}<br>\\\" +\",\n \"conf_interval = np.sort(conf_interval) percentile = np.sort(percentile) for k, v in default_values.items(): kwargs.setdefault(k, v)\",\n \"Amplitude: %{y:.2e}\\\" ), **kwargs, ) ) for i, p in enumerate(conf_interval): p1 =\",\n \"optional Sequence of confidence intervals to compute, which must be between 0 and\",\n \"\\\"\\\"\\\"Plot phase angle response. This method plots the phase response given an output\",\n \"return fig def plot(self, percentile=[], conf_interval=[], units=\\\"mic-pk-pk\\\", **kwargs): \\\"\\\"\\\"Plot frequency response. This method\",\n \"the frequency response magnitude given an output and an input using Plotly. Parameters\",\n \"/ 2, axis=0) fig.add_trace( go.Scatter( x=x, y=np.concatenate((p1, p2[::-1])), mode=\\\"lines\\\", line=dict(width=1, color=colors2[color_i]), fill=\\\"toself\\\", fillcolor=colors2[color_i],\",\n \"Figure Reference for more information. Returns ------- fig : Plotly graph_objects.Figure() The figure\",\n \"freedom to observe the response. percentile : list, optional Sequence of percentiles to\",\n \"linecolor=\\\"black\\\", mirror=True, ) fig.update_yaxes( title_text=\\\"<b>Damped Natural Frequencies</b>\\\", title_font=dict(family=\\\"Arial\\\", size=20), tickfont=dict(size=16), gridcolor=\\\"lightgray\\\", showline=True, linewidth=2.5,\",\n \"y=np.percentile(self.yout[..., ndof * n], 50 - p / 2, axis=0), z=np.percentile( self.yout[..., ndof\",\n \"axis=0), opacity=0.6, line=dict(width=2.5, color=colors2[i]), name=\\\"percentile: {}%\\\".format(p), hovertemplate=( \\\"X - Amplitude: %{x:.2e}<br>\\\" + \\\"Y\",\n \"a set of probes. Parameters ---------- probe : list of tuples List with\",\n \"each pair input/output. phase : array Array with the frequencies, phase of the\",\n \"\\\"mic-pk-pk\\\": r_axis_label = \\\"<b>Amplitude (μ pk-pk)</b>\\\" else: r_axis_label = \\\"<b>Amplitude (dB)</b>\\\" if fig\",\n \"3D view. Parameters ---------- percentile : list, optional Sequence of percentiles to compute,\",\n \"method plots the phase response given a set of probes. Parameters ---------- probe\",\n \"return fig def plot_log_dec(self, percentile=[], conf_interval=[], harmonics=[1], **kwargs): \\\"\\\"\\\"Plot the log_dec vs frequency.\",\n \"*args, **kwargs): \\\"\\\"\\\"Plot orbit response (3D). This function plots orbits for each node\",\n \"method plots the phase response given an output and an input using bokeh.\",\n \"\\\"\\\"\\\"Plot frequency response. This method plots the phase response given a set of\",\n \"self.speed_range[::-1])) for j in range(self.log_dec.shape[0]): fig.add_trace( go.Scatter( x=self.speed_range, y=np.mean(self.log_dec[j], axis=1), opacity=1.0, name=\\\"Mean -\",\n \"for more information. Returns ------- fig : Plotly graph_objects.Figure() Bokeh plot axes with\",\n \"the node where the probe is located. orientation : float, probe orientation angle\",\n \"p / 2, axis=1) p2 = np.percentile(self.wd[j], 50 - p / 2, axis=1)\",\n \"default_values.items(): kwargs.setdefault(k, v) fig = go.Figure() x = np.concatenate((self.speed_range, self.speed_range[::-1])) for j in\",\n \"zaxis=dict(title=dict(text=\\\"<b>Amplitude - Y</b>\\\"), showspikes=False), ), ) return fig class ST_ForcedResponseResults: \\\"\\\"\\\"Store stochastic results\",\n \"* h, opacity=1.0, name=\\\"{}x speed\\\".format(h), line=dict(width=3, color=colors1[j], dash=\\\"dashdot\\\"), legendgroup=\\\"speed{}\\\".format(j), hovertemplate=(\\\"Frequency: %{x:.3f}<br>\\\" + \\\"Frequency:\",\n \"graph_objects.make_subplots() Plotly figure with amplitude vs frequency phase angle vs frequency. \\\"\\\"\\\" def\",\n \"node for each frequency. number_dof = int Number of degrees of freedom per\",\n \"response. xout : array Time evolution of the state vector. nodes_list: array list\",\n \"+ p / 2, axis=0 ), line=dict(width=3.5, color=colors1[i]), opacity=0.6, name=\\\"confidence interval: {}%\\\".format(p), legendgroup=\\\"conf_interval{}\\\".format(p),\",\n \"+ \\\"Y - Amplitude: %{z:.2e}\\\" ), **kwargs, ) ) fig.add_trace( go.Scatter3d( x=x, y=np.percentile(self.yout[...,\",\n \"fig=None, units=\\\"mic-pk-pk\\\", **kwargs, ): \\\"\\\"\\\"Plot frequency response. This method plots the unbalance response\",\n \"self.speed_range[::-1])) for i, p in enumerate(conf_interval): p1 = np.percentile(self.phase, 50 + p /\",\n \"freedom. units : str Magnitude unit system. Default is \\\"mic-pk-pk\\\" polar_kwargs : optional\",\n \"decrement Returns ------- subplots : Plotly graph_objects.make_subplots() Plotly figure with diagrams for frequency\",\n \"1], axis=0), line=dict(width=5, color=\\\"black\\\"), name=\\\"Mean\\\", legendgroup=\\\"mean\\\", showlegend=True if j == 0 else False,\",\n \"with the plot. \\\"\\\"\\\" if fig is None: fig = go.Figure() default_values =\",\n \") ) for i, p in enumerate(percentile): fig.add_trace( go.Scatter( x=self.speed_range, y=np.percentile(self.log_dec[j], p, axis=1),\",\n \") color_p += 1 x = np.concatenate((self.frequency_range, self.frequency_range[::-1])) for j, p in enumerate(conf_interval):\",\n \"x=x, y=np.concatenate((p1, p2[::-1])), line=dict(width=1, color=colors1[j]), fill=\\\"toself\\\", fillcolor=colors1[j], opacity=0.3, name=\\\"confidence interval: {}% - Mode\"\n]","isConversational":false}}},{"rowIdx":3470,"cells":{"ngram":{"kind":"list like","value":["for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f,","range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn]","next turn, etc. # For example, it might return # # [[0 0","have been 3 turns, and we have defected twice then cooperated once, #","scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\")","= next turn, etc. # For example, it might return # # [[0","betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload files,","np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i]","def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar =","= [[1,5],[0,3]] # The i-j-th element of this array is how many points","np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] #","f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for t in range(roundHistory.shape[1]):","if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\")","runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST =","(%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results file written to \"+RESULTS_FILE) runFullPairingTournament(STRATEGY_FOLDER,","we have defected twice then cooperated once, # and our opponent has cooperated","if there have been 3 turns, and we have defected twice then cooperated","[[0 0 1] a.k.a. D D C # [1 1 1]] a.k.a. C","etc. # C = cooperate, stay silent, comply, upload files, etc. # Returns","= open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB =","history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0","scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]]","turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar","= moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA","return # # [[0 0 1] a.k.a. D D C # [1 1","= runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] +=","D = defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent,","+= pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA,","this array is how many points you receive if you do play i,","= \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is","= {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for","= first turn, 1 = next turn, etc. # For example, it might","scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for","once, # and our opponent has cooperated all three times. def getVisibleHistory(history, player,","import importlib import numpy as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE =","SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer =","\"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri for i in range(len(stri),leng):","f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\")","guarantees that every turn after the 50th has an equal (low) chance of","(0 = us, 1 = opponent) # The second axis is which turn.","every turn after the 50th has an equal (low) chance of being the","playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] =","array is how many points you receive if you do play i, and","leng): result = stri for i in range(len(stri),leng): result = result+\" \" return","files, etc. # Returns a 2-by-n numpy array. The first axis is which","score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng):","\"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is how","free-ride, etc. # C = cooperate, stay silent, comply, upload files, etc. #","[[1,5],[0,3]] # The i-j-th element of this array is how many points you","which turn. (0 = first turn, 1 = next turn, etc. # For","result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder)","= [] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST:","and your opponent does play j. moveLabels = [\"D\",\"C\"] # D = defect,","if you do play i, and your opponent does play j. moveLabels =","for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1)","that every turn after the 50th has an equal (low) chance of being","result = result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files","for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result","tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy","for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \")","range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score","rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\":","return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None","outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = []","= rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\")","def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn","# For example, it might return # # [[0 0 1] a.k.a. D","= us, 1 = opponent) # The second axis is which turn. (0","you do play i, and your opponent does play j. moveLabels = [\"D\",\"C\"]","return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper =","turn. (0 = first turn, 1 = next turn, etc. # For example,","axis is which turn. (0 = first turn, 1 = next turn, etc.","a.k.a. C C C # # if there have been 3 turns, and","# # [[0 0 1] a.k.a. D D C # [1 1 1]]","range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)):","# [[0 0 1] a.k.a. D D C # [1 1 1]] a.k.a.","pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair,","etc. # Returns a 2-by-n numpy array. The first axis is which player","are a minimum of 50 turns long. The np.log here guarantees that every","a minimum of 50 turns long. The np.log here guarantees that every turn","[] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy]","moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME","= defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply,","player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0])","an equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for","D C # [1 1 1]] a.k.a. C C C # # if","for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn]","pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS.","historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def","\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\".py\"):","STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair","C C C # # if there have been 3 turns, and we","numpy array. The first axis is which player (0 = us, 1 =","etc. # For example, it might return # # [[0 0 1] a.k.a.","history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB","C # [1 1 1]] a.k.a. C C C # # if there","you receive if you do play i, and your opponent does play j.","in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f","\"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results file written to","score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri for i","history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA +=","has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if","itertools import importlib import numpy as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE","%.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results file written to \"+RESULTS_FILE)","= score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results","random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th","The i-j-th element of this array is how many points you receive if","= tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB","times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar","STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory","= history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f,","range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+'","0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA,","in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory,","1 = opponent) # The second axis is which turn. (0 = first","turn, 1 = next turn, etc. # For example, it might return #","= scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done","long. The np.log here guarantees that every turn after the 50th has an","silent, comply, upload files, etc. # Returns a 2-by-n numpy array. The first","memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are","np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB)","\"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this","stri for i in range(len(stri),leng): result = result+\" \" return result def runFullPairingTournament(inFolder,","do play i, and your opponent does play j. moveLabels = [\"D\",\"C\"] #","score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results file","in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2):","# D = defect, betray, sabotage, free-ride, etc. # C = cooperate, stay","= moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return","scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH):","i, and your opponent does play j. moveLabels = [\"D\",\"C\"] # D =","# The i-j-th element of this array is how many points you receive","2-by-n numpy array. The first axis is which player (0 = us, 1","moveLabels = [\"D\",\"C\"] # D = defect, betray, sabotage, free-ride, etc. # C","\"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result =","\"+pair[1]+\" (P2)\\n\") for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t]","moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) #","playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def","os import itertools import importlib import numpy as np import random STRATEGY_FOLDER =","receive if you do play i, and your opponent does play j. moveLabels","scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)):","playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH =","scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3])","= history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH,","your opponent does play j. moveLabels = [\"D\",\"C\"] # D = defect, betray,","array. The first axis is which player (0 = us, 1 = opponent)","in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in","outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in","= np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score =","np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\")","f.write(\"\\n\") def pad(stri, leng): result = stri for i in range(len(stri),leng): result =","score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close()","# The games are a minimum of 50 turns long. The np.log here","# [1 1 1]] a.k.a. C C C # # if there have","scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] =","three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1:","+= pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1)","scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\"","= scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i =","after the 50th has an equal (low) chance of being the final turn.","in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final","average)\\n\") f.flush() f.close() print(\"Done with everything! Results file written to \"+RESULTS_FILE) runFullPairingTournament(STRATEGY_FOLDER, RESULTS_FILE)","= history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair):","i-j-th element of this array is how many points you receive if you","None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum","None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50 turns","rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score","points you receive if you do play i, and your opponent does play","50th has an equal (low) chance of being the final turn. history =","the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA =","= \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element of","there have been 3 turns, and we have defected twice then cooperated once,","RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array","move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score","historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1])","# The second axis is which turn. (0 = first turn, 1 =","files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder):","= opponent) # The second axis is which turn. (0 = first turn,","(P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for t in range(roundHistory.shape[1]): move","3 turns, and we have defected twice then cooperated once, # and our","j. moveLabels = [\"D\",\"C\"] # D = defect, betray, sabotage, free-ride, etc. #","memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history):","defected twice then cooperated once, # and our opponent has cooperated all three","numpy as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray =","f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB","range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove]","player (0 = us, 1 = opponent) # The second axis is which","STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The i-j-th element","# C = cooperate, stay silent, comply, upload files, etc. # Returns a","it might return # # [[0 0 1] a.k.a. D D C #","[1 1 1]] a.k.a. C C C # # if there have been","\") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\")","does play j. moveLabels = [\"D\",\"C\"] # D = defect, betray, sabotage, free-ride,","int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long. The np.log","us, 1 = opponent) # The second axis is which turn. (0 =","== 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB","is which player (0 = us, 1 = opponent) # The second axis","cooperated once, # and our opponent has cooperated all three times. def getVisibleHistory(history,","comply, upload files, etc. # Returns a 2-by-n numpy array. The first axis","# and our opponent has cooperated all three times. def getVisibleHistory(history, player, turn):","for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for","games are a minimum of 50 turns long. The np.log here guarantees that","the 50th has an equal (low) chance of being the final turn. history","range(len(stri),leng): result = result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading","scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in","final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA)","importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games","might return # # [[0 0 1] a.k.a. D D C # [1","scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i","turns, and we have defected twice then cooperated once, # and our opponent","sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload files, etc.","for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove]","50 turns long. The np.log here guarantees that every turn after the 50th","tournament, reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file","turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] =","result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper = {}","i in range(len(stri),leng): result = result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting","1 = next turn, etc. # For example, it might return # #","p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\")","axis is which player (0 = us, 1 = opponent) # The second","0 1] a.k.a. D D C # [1 1 1]] a.k.a. C C","for i in range(len(stri),leng): result = result+\" \" return result def runFullPairingTournament(inFolder, outFile):","second axis is which turn. (0 = first turn, 1 = next turn,","= importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The","in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+'","in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\")","scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank]","memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of","= np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB =","pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is how many","# # if there have been 3 turns, and we have defected twice","roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]]","roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\":","C = cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n","= cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n numpy","for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] =","0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove =","cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n numpy array.","history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH","outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy =","if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA =","= importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME =","C C # # if there have been 3 turns, and we have","1] a.k.a. D D C # [1 1 1]] a.k.a. C C C","as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]]","opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy()","1 1]] a.k.a. C C C # # if there have been 3","import numpy as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray","opponent does play j. moveLabels = [\"D\",\"C\"] # D = defect, betray, sabotage,","i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\"","import itertools import importlib import numpy as np import random STRATEGY_FOLDER = \"exampleStrats\"","and our opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar","= None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a","The second axis is which turn. (0 = first turn, 1 = next","= 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove","= result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from","strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST,","= 0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair)","f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri for","history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0","VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for t in range(roundHistory.shape[1]): move =","\"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri","import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\" pointsArray = [[1,5],[0,3]] # The","\"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri for i in range(len(stri),leng): result","scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory =","np.log here guarantees that every turn after the 50th has an equal (low)","turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove,","import os import itertools import importlib import numpy as np import random STRATEGY_FOLDER","= playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH","opponent) # The second axis is which turn. (0 = first turn, 1","(low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in","here guarantees that every turn after the 50th has an equal (low) chance","turn after the 50th has an equal (low) chance of being the final","+= scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings","pad(stri, leng): result = stri for i in range(len(stri),leng): result = result+\" \"","scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings =","reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in","stay silent, comply, upload files, etc. # Returns a 2-by-n numpy array. The","# if there have been 3 turns, and we have defected twice then","def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST","play j. moveLabels = [\"D\",\"C\"] # D = defect, betray, sabotage, free-ride, etc.","twice then cooperated once, # and our opponent has cooperated all three times.","a 2-by-n numpy array. The first axis is which player (0 = us,","def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB =","Returns a 2-by-n numpy array. The first axis is which player (0 =","STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in","our opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar =","result = stri for i in range(len(stri),leng): result = result+\" \" return result","i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank","first axis is which player (0 = us, 1 = opponent) # The","getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0)","playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB =","[\"D\",\"C\"] # D = defect, betray, sabotage, free-ride, etc. # C = cooperate,","play i, and your opponent does play j. moveLabels = [\"D\",\"C\"] # D","in range(len(stri),leng): result = result+\" \" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament,","then cooperated once, # and our opponent has cooperated all three times. def","1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB =","historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB","t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\":","is which turn. (0 = first turn, 1 = next turn, etc. #","pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB):","have defected twice then cooperated once, # and our opponent has cooperated all","all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player ==","many points you receive if you do play i, and your opponent does","np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA =","scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with","return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1]","player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return","LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long.","cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player","= stri for i in range(len(stri),leng): result = result+\" \" return result def","turns long. The np.log here guarantees that every turn after the 50th has","= np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA","file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for","\" return result def runFullPairingTournament(inFolder, outFile): print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper","f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri,","f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def","open(outFile,\"w+\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory)","is how many points you receive if you do play i, and your","{} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy","turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB","roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for","print(\"Starting tournament, reading files from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for","D D C # [1 1 1]] a.k.a. C C C # #","# Returns a 2-by-n numpy array. The first axis is which player (0","being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA","been 3 turns, and we have defected twice then cooperated once, # and","has an equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int)","scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for t in","= 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove","playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return","= None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50","C # # if there have been 3 turns, and we have defected","os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f =","history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA","f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything! Results file written","and we have defected twice then cooperated once, # and our opponent has","The np.log here guarantees that every turn after the 50th has an equal","pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2):","example, it might return # # [[0 0 1] a.k.a. D D C","file in os.listdir(inFolder): if file.endswith(\".py\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0","def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p","moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA =","of this array is how many points you receive if you do play","importlib import numpy as np import random STRATEGY_FOLDER = \"exampleStrats\" RESULTS_FILE = \"results.txt\"","scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy)","The first axis is which player (0 = us, 1 = opponent) #","how many points you receive if you do play i, and your opponent","scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn]","playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def","history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair,","element of this array is how many points you receive if you do","minimum of 50 turns long. The np.log here guarantees that every turn after","= [\"D\",\"C\"] # D = defect, betray, sabotage, free-ride, etc. # C =","f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer","The games are a minimum of 50 turns long. The np.log here guarantees","memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove","itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA,","turn, etc. # For example, it might return # # [[0 0 1]","moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history","range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final","ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn]","roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for","which player (0 = us, 1 = opponent) # The second axis is","scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush() f.close() print(\"Done with everything!","pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper))","= roundHistory[p,t] f.write(moveLabels[move]+\" \") f.write(\"\\n\") f.write(\"Final score for \"+pair[0]+\": \"+str(scoresA)+\"\\n\") f.write(\"Final score for","upload files, etc. # Returns a 2-by-n numpy array. The first axis is","= playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB","def pad(stri, leng): result = stri for i in range(len(stri),leng): result = result+\"","(0 = first turn, 1 = next turn, etc. # For example, it","of 50 turns long. The np.log here guarantees that every turn after the","= int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long. The","rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\"#\"+str(rank+1)+\": \"+pad(STRATEGY_LIST[i]+\":\",16)+' %.3f'%score+' (%.3f'%scorePer+\" average)\\n\") f.flush()","importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random()))","scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\" (P2)\\n\") for p in range(2): for t","in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove","runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\".\"+pair[1]) memoryA = None memoryB = None","from \"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if","scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for rank in range(len(STRATEGY_LIST)): i","return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\" (P1) VS. \"+pair[1]+\"","scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]]","For example, it might return # # [[0 0 1] a.k.a. D D","scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] +=","for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL SCORES\\n\") for","defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload","1]] a.k.a. C C C # # if there have been 3 turns,","equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn","tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in","(P2)\\n\") for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\"","scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory,","+= scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i]","r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB)","in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB +=","history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for","first turn, 1 = next turn, etc. # For example, it might return","0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove =","a.k.a. D D C # [1 1 1]] a.k.a. C C C #","for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\"w+\") for pair in","= np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\"\\n\\nTOTAL","of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove,","for \"+pair[1]+\": \"+str(scoresB)+\"\\n\") f.write(\"\\n\") def pad(stri, leng): result = stri for i in","runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA","chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME):","= history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA"],"string":"[\n \"for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f,\",\n \"range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn]\",\n \"next turn, etc. # For example, it might return # # [[0 0\",\n \"have been 3 turns, and we have defected twice then cooperated once, #\",\n \"scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\")\",\n \"= next turn, etc. # For example, it might return # # [[0\",\n \"betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload files,\",\n \"np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i]\",\n \"def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar =\",\n \"= [[1,5],[0,3]] # The i-j-th element of this array is how many points\",\n \"np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] #\",\n \"f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t in range(roundHistory.shape[1]):\",\n \"if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\")\",\n \"runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST =\",\n \"(%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file written to \\\"+RESULTS_FILE) runFullPairingTournament(STRATEGY_FOLDER,\",\n \"we have defected twice then cooperated once, # and our opponent has cooperated\",\n \"if there have been 3 turns, and we have defected twice then cooperated\",\n \"[[0 0 1] a.k.a. D D C # [1 1 1]] a.k.a. C\",\n \"etc. # C = cooperate, stay silent, comply, upload files, etc. # Returns\",\n \"= open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB =\",\n \"history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0\",\n \"scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]]\",\n \"turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar\",\n \"= moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA\",\n \"return # # [[0 0 1] a.k.a. D D C # [1 1\",\n \"= runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] +=\",\n \"D = defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent,\",\n \"+= pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA,\",\n \"this array is how many points you receive if you do play i,\",\n \"= \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is\",\n \"= {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for\",\n \"= first turn, 1 = next turn, etc. # For example, it might\",\n \"scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for\",\n \"once, # and our opponent has cooperated all three times. def getVisibleHistory(history, player,\",\n \"import importlib import numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE =\",\n \"SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer =\",\n \"\\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for i in range(len(stri),leng):\",\n \"f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\")\",\n \"guarantees that every turn after the 50th has an equal (low) chance of\",\n \"(0 = us, 1 = opponent) # The second axis is which turn.\",\n \"every turn after the 50th has an equal (low) chance of being the\",\n \"playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] =\",\n \"array is how many points you receive if you do play i, and\",\n \"leng): result = stri for i in range(len(stri),leng): result = result+\\\" \\\" return\",\n \"files, etc. # Returns a 2-by-n numpy array. The first axis is which\",\n \"score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng):\",\n \"\\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is how\",\n \"free-ride, etc. # C = cooperate, stay silent, comply, upload files, etc. #\",\n \"[[1,5],[0,3]] # The i-j-th element of this array is how many points you\",\n \"which turn. (0 = first turn, 1 = next turn, etc. # For\",\n \"result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder)\",\n \"= [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST:\",\n \"and your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect,\",\n \"if you do play i, and your opponent does play j. moveLabels =\",\n \"for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1)\",\n \"that every turn after the 50th has an equal (low) chance of being\",\n \"result = result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files\",\n \"for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result\",\n \"tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy\",\n \"for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\")\",\n \"range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score\",\n \"rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\":\",\n \"return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None\",\n \"outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = []\",\n \"= rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\")\",\n \"def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn\",\n \"# For example, it might return # # [[0 0 1] a.k.a. D\",\n \"= us, 1 = opponent) # The second axis is which turn. (0\",\n \"you do play i, and your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"]\",\n \"return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper =\",\n \"turn. (0 = first turn, 1 = next turn, etc. # For example,\",\n \"axis is which turn. (0 = first turn, 1 = next turn, etc.\",\n \"a.k.a. C C C # # if there have been 3 turns, and\",\n \"# # [[0 0 1] a.k.a. D D C # [1 1 1]]\",\n \"range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)):\",\n \"# [[0 0 1] a.k.a. D D C # [1 1 1]] a.k.a.\",\n \"pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair,\",\n \"etc. # Returns a 2-by-n numpy array. The first axis is which player\",\n \"are a minimum of 50 turns long. The np.log here guarantees that every\",\n \"a minimum of 50 turns long. The np.log here guarantees that every turn\",\n \"[] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy]\",\n \"moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME\",\n \"= defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply,\",\n \"player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0])\",\n \"an equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for\",\n \"D C # [1 1 1]] a.k.a. C C C # # if\",\n \"for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn]\",\n \"pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS.\",\n \"historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def\",\n \"\\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"):\",\n \"STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair\",\n \"C C C # # if there have been 3 turns, and we\",\n \"numpy array. The first axis is which player (0 = us, 1 =\",\n \"etc. # For example, it might return # # [[0 0 1] a.k.a.\",\n \"history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB\",\n \"C # [1 1 1]] a.k.a. C C C # # if there\",\n \"you receive if you do play i, and your opponent does play j.\",\n \"in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f\",\n \"\\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file written to\",\n \"score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for i\",\n \"history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA +=\",\n \"has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if\",\n \"itertools import importlib import numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE\",\n \"%.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file written to \\\"+RESULTS_FILE)\",\n \"= score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results\",\n \"random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th\",\n \"The i-j-th element of this array is how many points you receive if\",\n \"= tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB\",\n \"times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar\",\n \"STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory\",\n \"= history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f,\",\n \"range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+'\",\n \"0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA,\",\n \"in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory,\",\n \"1 = opponent) # The second axis is which turn. (0 = first\",\n \"turn, 1 = next turn, etc. # For example, it might return #\",\n \"= scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done\",\n \"long. The np.log here guarantees that every turn after the 50th has an\",\n \"silent, comply, upload files, etc. # Returns a 2-by-n numpy array. The first\",\n \"memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are\",\n \"np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB)\",\n \"\\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this\",\n \"stri for i in range(len(stri),leng): result = result+\\\" \\\" return result def runFullPairingTournament(inFolder,\",\n \"do play i, and your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] #\",\n \"score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file\",\n \"in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2):\",\n \"# D = defect, betray, sabotage, free-ride, etc. # C = cooperate, stay\",\n \"= moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return\",\n \"scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH):\",\n \"i, and your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D =\",\n \"# The i-j-th element of this array is how many points you receive\",\n \"2-by-n numpy array. The first axis is which player (0 = us, 1\",\n \"moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride, etc. # C\",\n \"\\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result =\",\n \"\\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t]\",\n \"moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) #\",\n \"playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def\",\n \"os import itertools import importlib import numpy as np import random STRATEGY_FOLDER =\",\n \"receive if you do play i, and your opponent does play j. moveLabels\",\n \"scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)):\",\n \"playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH =\",\n \"scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3])\",\n \"= history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH,\",\n \"your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray,\",\n \"array. The first axis is which player (0 = us, 1 = opponent)\",\n \"in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in\",\n \"outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in\",\n \"= np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score =\",\n \"np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\")\",\n \"f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for i in range(len(stri),leng): result =\",\n \"score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close()\",\n \"# The games are a minimum of 50 turns long. The np.log here\",\n \"# [1 1 1]] a.k.a. C C C # # if there have\",\n \"scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] =\",\n \"three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1:\",\n \"+= pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1)\",\n \"scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\"\",\n \"= scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i =\",\n \"after the 50th has an equal (low) chance of being the final turn.\",\n \"in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final\",\n \"average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file written to \\\"+RESULTS_FILE) runFullPairingTournament(STRATEGY_FOLDER, RESULTS_FILE)\",\n \"= history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair):\",\n \"i-j-th element of this array is how many points you receive if you\",\n \"None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum\",\n \"None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50 turns\",\n \"rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score\",\n \"points you receive if you do play i, and your opponent does play\",\n \"50th has an equal (low) chance of being the final turn. history =\",\n \"the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA =\",\n \"= \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element of\",\n \"there have been 3 turns, and we have defected twice then cooperated once,\",\n \"RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element of this array\",\n \"move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score\",\n \"historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1])\",\n \"# The second axis is which turn. (0 = first turn, 1 =\",\n \"files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder):\",\n \"= opponent) # The second axis is which turn. (0 = first turn,\",\n \"(P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t in range(roundHistory.shape[1]): move\",\n \"3 turns, and we have defected twice then cooperated once, # and our\",\n \"j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride, etc. #\",\n \"memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history):\",\n \"defected twice then cooperated once, # and our opponent has cooperated all three\",\n \"numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray =\",\n \"f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB\",\n \"range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove]\",\n \"player (0 = us, 1 = opponent) # The second axis is which\",\n \"STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The i-j-th element\",\n \"# C = cooperate, stay silent, comply, upload files, etc. # Returns a\",\n \"it might return # # [[0 0 1] a.k.a. D D C #\",\n \"[1 1 1]] a.k.a. C C C # # if there have been\",\n \"\\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\")\",\n \"does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride,\",\n \"int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long. The np.log\",\n \"us, 1 = opponent) # The second axis is which turn. (0 =\",\n \"== 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB\",\n \"is which player (0 = us, 1 = opponent) # The second axis\",\n \"cooperated once, # and our opponent has cooperated all three times. def getVisibleHistory(history,\",\n \"comply, upload files, etc. # Returns a 2-by-n numpy array. The first axis\",\n \"# and our opponent has cooperated all three times. def getVisibleHistory(history, player, turn):\",\n \"for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for\",\n \"games are a minimum of 50 turns long. The np.log here guarantees that\",\n \"the 50th has an equal (low) chance of being the final turn. history\",\n \"range(len(stri),leng): result = result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading\",\n \"scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in\",\n \"final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA)\",\n \"importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games\",\n \"might return # # [[0 0 1] a.k.a. D D C # [1\",\n \"scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i\",\n \"turns, and we have defected twice then cooperated once, # and our opponent\",\n \"sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload files, etc.\",\n \"for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove]\",\n \"50 turns long. The np.log here guarantees that every turn after the 50th\",\n \"tournament, reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file\",\n \"turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] =\",\n \"result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper = {}\",\n \"i in range(len(stri),leng): result = result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting\",\n \"1 = next turn, etc. # For example, it might return # #\",\n \"p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\")\",\n \"axis is which player (0 = us, 1 = opponent) # The second\",\n \"0 1] a.k.a. D D C # [1 1 1]] a.k.a. C C\",\n \"for i in range(len(stri),leng): result = result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile):\",\n \"second axis is which turn. (0 = first turn, 1 = next turn,\",\n \"= importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The\",\n \"in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+'\",\n \"in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\")\",\n \"scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank]\",\n \"memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of\",\n \"= np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB =\",\n \"pointsArray = [[1,5],[0,3]] # The i-j-th element of this array is how many\",\n \"# # if there have been 3 turns, and we have defected twice\",\n \"roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]]\",\n \"roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\":\",\n \"C = cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n\",\n \"= cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n numpy\",\n \"for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] =\",\n \"0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove =\",\n \"cooperate, stay silent, comply, upload files, etc. # Returns a 2-by-n numpy array.\",\n \"history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH\",\n \"outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy =\",\n \"if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA =\",\n \"= importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME =\",\n \"C C # # if there have been 3 turns, and we have\",\n \"1] a.k.a. D D C # [1 1 1]] a.k.a. C C C\",\n \"as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]]\",\n \"opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy()\",\n \"1 1]] a.k.a. C C C # # if there have been 3\",\n \"import numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray\",\n \"opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage,\",\n \"i = rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\"\",\n \"import itertools import importlib import numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\"\",\n \"and our opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar\",\n \"= None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a\",\n \"The second axis is which turn. (0 = first turn, 1 = next\",\n \"= 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove\",\n \"= result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from\",\n \"strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST,\",\n \"= 0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair)\",\n \"f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for\",\n \"history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0\",\n \"VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t in range(roundHistory.shape[1]): move =\",\n \"\\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri\",\n \"import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\" pointsArray = [[1,5],[0,3]] # The\",\n \"\\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for i in range(len(stri),leng): result\",\n \"scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory =\",\n \"np.log here guarantees that every turn after the 50th has an equal (low)\",\n \"turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove,\",\n \"import os import itertools import importlib import numpy as np import random STRATEGY_FOLDER\",\n \"= playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH\",\n \"opponent) # The second axis is which turn. (0 = first turn, 1\",\n \"(low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in\",\n \"here guarantees that every turn after the 50th has an equal (low) chance\",\n \"turn after the 50th has an equal (low) chance of being the final\",\n \"+= scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings\",\n \"pad(stri, leng): result = stri for i in range(len(stri),leng): result = result+\\\" \\\"\",\n \"scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings =\",\n \"reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in\",\n \"stay silent, comply, upload files, etc. # Returns a 2-by-n numpy array. The\",\n \"# if there have been 3 turns, and we have defected twice then\",\n \"def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST\",\n \"play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride, etc.\",\n \"twice then cooperated once, # and our opponent has cooperated all three times.\",\n \"a 2-by-n numpy array. The first axis is which player (0 = us,\",\n \"def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB =\",\n \"Returns a 2-by-n numpy array. The first axis is which player (0 =\",\n \"STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in\",\n \"our opponent has cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar =\",\n \"result = stri for i in range(len(stri),leng): result = result+\\\" \\\" return result\",\n \"i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank\",\n \"first axis is which player (0 = us, 1 = opponent) # The\",\n \"getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0)\",\n \"playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB =\",\n \"[\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride, etc. # C = cooperate,\",\n \"play i, and your opponent does play j. moveLabels = [\\\"D\\\",\\\"C\\\"] # D\",\n \"in range(len(stri),leng): result = result+\\\" \\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament,\",\n \"then cooperated once, # and our opponent has cooperated all three times. def\",\n \"1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB =\",\n \"historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB\",\n \"t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\":\",\n \"is which turn. (0 = first turn, 1 = next turn, etc. #\",\n \"pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB):\",\n \"have defected twice then cooperated once, # and our opponent has cooperated all\",\n \"all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player ==\",\n \"many points you receive if you do play i, and your opponent does\",\n \"np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA =\",\n \"scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with\",\n \"return history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1]\",\n \"player, turn): historySoFar = history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return\",\n \"LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long.\",\n \"cooperated all three times. def getVisibleHistory(history, player, turn): historySoFar = history[:,:turn].copy() if player\",\n \"= stri for i in range(len(stri),leng): result = result+\\\" \\\" return result def\",\n \"turns long. The np.log here guarantees that every turn after the 50th has\",\n \"= np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA\",\n \"file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for\",\n \"\\\" return result def runFullPairingTournament(inFolder, outFile): print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper\",\n \"f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri,\",\n \"f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def\",\n \"open(outFile,\\\"w+\\\") for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory)\",\n \"is how many points you receive if you do play i, and your\",\n \"{} STRATEGY_LIST = [] for file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy\",\n \"turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB\",\n \"roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for\",\n \"print(\\\"Starting tournament, reading files from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for\",\n \"D D C # [1 1 1]] a.k.a. C C C # #\",\n \"# Returns a 2-by-n numpy array. The first axis is which player (0\",\n \"being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove, memoryA\",\n \"been 3 turns, and we have defected twice then cooperated once, # and\",\n \"has an equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int)\",\n \"scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t in\",\n \"= 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove\",\n \"playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return\",\n \"= None LENGTH_OF_GAME = int(200-40*np.log(random.random())) # The games are a minimum of 50\",\n \"C # # if there have been 3 turns, and we have defected\",\n \"os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f =\",\n \"history[:,:turn].copy() if player == 1: historySoFar = np.flip(historySoFar,0) return historySoFar def runRound(pair): moduleA\",\n \"f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything! Results file written\",\n \"and we have defected twice then cooperated once, # and our opponent has\",\n \"The np.log here guarantees that every turn after the 50th has an equal\",\n \"pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2):\",\n \"example, it might return # # [[0 0 1] a.k.a. D D C\",\n \"file in os.listdir(inFolder): if file.endswith(\\\".py\\\"): STRATEGY_LIST.append(file[:-3]) for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0\",\n \"def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p\",\n \"moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA =\",\n \"of this array is how many points you receive if you do play\",\n \"importlib import numpy as np import random STRATEGY_FOLDER = \\\"exampleStrats\\\" RESULTS_FILE = \\\"results.txt\\\"\",\n \"scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy)\",\n \"The first axis is which player (0 = us, 1 = opponent) #\",\n \"how many points you receive if you do play i, and your opponent\",\n \"scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn]\",\n \"playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def\",\n \"history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair,\",\n \"element of this array is how many points you receive if you do\",\n \"minimum of 50 turns long. The np.log here guarantees that every turn after\",\n \"= [\\\"D\\\",\\\"C\\\"] # D = defect, betray, sabotage, free-ride, etc. # C =\",\n \"f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i = rankings[-1-rank] score = scoresNumpy[i] scorePer\",\n \"The games are a minimum of 50 turns long. The np.log here guarantees\",\n \"memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove\",\n \"itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA,\",\n \"turn, etc. # For example, it might return # # [[0 0 1]\",\n \"moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove history[1,turn] = playerBmove return history\",\n \"range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final\",\n \"ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn]\",\n \"roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for\",\n \"which player (0 = us, 1 = opponent) # The second axis is\",\n \"scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush() f.close() print(\\\"Done with everything!\",\n \"pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper))\",\n \"= roundHistory[p,t] f.write(moveLabels[move]+\\\" \\\") f.write(\\\"\\\\n\\\") f.write(\\\"Final score for \\\"+pair[0]+\\\": \\\"+str(scoresA)+\\\"\\\\n\\\") f.write(\\\"Final score for\",\n \"upload files, etc. # Returns a 2-by-n numpy array. The first axis is\",\n \"= playerAmove history[1,turn] = playerBmove return history def tallyRoundScores(history): scoreA = 0 scoreB\",\n \"def pad(stri, leng): result = stri for i in range(len(stri),leng): result = result+\\\"\",\n \"(0 = first turn, 1 = next turn, etc. # For example, it\",\n \"of 50 turns long. The np.log here guarantees that every turn after the\",\n \"= int(200-40*np.log(random.random())) # The games are a minimum of 50 turns long. The\",\n \"rankings[-1-rank] score = scoresNumpy[i] scorePer = score/(len(STRATEGY_LIST)-1) f.write(\\\"#\\\"+str(rank+1)+\\\": \\\"+pad(STRATEGY_LIST[i]+\\\":\\\",16)+' %.3f'%score+' (%.3f'%scorePer+\\\" average)\\\\n\\\") f.flush()\",\n \"importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None LENGTH_OF_GAME = int(200-40*np.log(random.random()))\",\n \"scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\" (P2)\\\\n\\\") for p in range(2): for t\",\n \"in range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVisibleHistory(history,1,turn),memoryB) history[0,turn] = playerAmove\",\n \"runRound(pair): moduleA = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[0]) moduleB = importlib.import_module(STRATEGY_FOLDER+\\\".\\\"+pair[1]) memoryA = None memoryB = None\",\n \"from \\\"+inFolder) scoreKeeper = {} STRATEGY_LIST = [] for file in os.listdir(inFolder): if\",\n \"scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for rank in range(len(STRATEGY_LIST)): i\",\n \"return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory, scoresA, scoresB): f.write(pair[0]+\\\" (P1) VS. \\\"+pair[1]+\\\"\",\n \"scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]]\",\n \"For example, it might return # # [[0 0 1] a.k.a. D D\",\n \"scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA scoreKeeper[pair[1]] +=\",\n \"for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL SCORES\\\\n\\\") for\",\n \"defect, betray, sabotage, free-ride, etc. # C = cooperate, stay silent, comply, upload\",\n \"1]] a.k.a. C C C # # if there have been 3 turns,\",\n \"equal (low) chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn\",\n \"tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for turn in\",\n \"(P2)\\\\n\\\") for p in range(2): for t in range(roundHistory.shape[1]): move = roundHistory[p,t] f.write(moveLabels[move]+\\\"\",\n \"scoreA += pointsArray[playerAmove][playerBmove] scoreB += pointsArray[playerBmove][playerAmove] return scoreA/ROUND_LENGTH, scoreB/ROUND_LENGTH def outputRoundResults(f, pair, roundHistory,\",\n \"+= scoresA scoreKeeper[pair[1]] += scoresB scoresNumpy = np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i]\",\n \"r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB)\",\n \"in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA += pointsArray[playerAmove][playerBmove] scoreB +=\",\n \"history def tallyRoundScores(history): scoreA = 0 scoreB = 0 ROUND_LENGTH = history.shape[1] for\",\n \"first turn, 1 = next turn, etc. # For example, it might return\",\n \"0 ROUND_LENGTH = history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove =\",\n \"a.k.a. D D C # [1 1 1]] a.k.a. C C C #\",\n \"for strategy in STRATEGY_LIST: scoreKeeper[strategy] = 0 f = open(outFile,\\\"w+\\\") for pair in\",\n \"= np.zeros(len(scoreKeeper)) for i in range(len(STRATEGY_LIST)): scoresNumpy[i] = scoreKeeper[STRATEGY_LIST[i]] rankings = np.argsort(scoresNumpy) f.write(\\\"\\\\n\\\\nTOTAL\",\n \"of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME): playerAmove,\",\n \"for \\\"+pair[1]+\\\": \\\"+str(scoresB)+\\\"\\\\n\\\") f.write(\\\"\\\\n\\\") def pad(stri, leng): result = stri for i in\",\n \"runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f, pair, roundHistory, scoresA, scoresB) scoreKeeper[pair[0]] += scoresA\",\n \"chance of being the final turn. history = np.zeros((2,LENGTH_OF_GAME),dtype=int) for turn in range(LENGTH_OF_GAME):\",\n \"= history.shape[1] for turn in range(ROUND_LENGTH): playerAmove = history[0,turn] playerBmove = history[1,turn] scoreA\"\n]","isConversational":false}}},{"rowIdx":3471,"cells":{"ngram":{"kind":"list like","value":["TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE","db: database to connect to within server :type db: string ''' # If","self.connection.cursor() # For if caller never exhausts the results by repeated calls: self.cursors.append(cursor)","BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED",":type colName: String :param newVal: value acceptable to MySQL for the given column","following disclaimer in the documentation and/or other materials provided with the distribution. #","= self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table","cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return a string","this list of conditions and the following disclaimer. # 2. Redistributions in binary","AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT","If all arguments are set to None, we are unittesting: if all(arg is","valueTupleArray: array of n-tuples, which hold the values. Order of\\ values must corresond","table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS","OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE","Create new table, given its name, and schema. The schema is a dict","to a temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute","fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd =","user self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection =","Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd =","''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host","colName: column whose value is to be changed :type colName: String :param newVal:","All rights reserved. # # Redistribution and use in source and binary forms,","software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY",":type port: int :param user: user to log in as :type user: string","never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes =","OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName,","SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY",":param schema: dictionary mapping column names to column types :type schema: Dict<String,String> '''","which hold the values. Order of\\ values must corresond to order of column","proper order, i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray:","names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in",":param port: MySQL host's port :type port: int :param user: user to log","COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,","except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new","column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor()","preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \"My Poem\"' Note that","or promote products derived from this software without specific prior written permission. #","subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to","are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user =","and the following disclaimer. # 2. Redistributions in binary form must reproduce the","types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new","'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type tableName: String","are updated to\\ the given value. Syntax must conform to what may be",":type colVals: <any> ''' resList = [] for el in colVals: if isinstance(el,","cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors","to update.\\ if None, the named column in all rows are updated to\\","''' cursor = self.connection.cursor() try: if fromCondition is None: cmd = \"UPDATE %s","dictionary mapping column names to column types :type schema: Dict<String,String> ''' colSpec =","currently still open. ''' for cursor in self.cursors: try: cursor.close() except: pass try:","a query, return one result for each subsequent call. :param queryStr: query :type",":type tblName: String :param colnameValueDict: mapping of column name to column value :type","@author: paepcke Modifications: - Dec 30, 2013: Added closing of connection to close()","string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \"My Poem\"'","name of table to insert into :type tblName: String :param colnameValueDict: mapping of","to MySQL for the given column :param fromCondition: optionally condition that selects which","# 2. Redistributions in binary form must reproduce the above copyright notice, this","colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ','","for colName, colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal)","to what may be in\\ a MySQL FROM clause (don't include the 'FROM'","to insert into :type tblName: String :param colnameValueDict: mapping of column name to","value acceptable to MySQL for the given column :type newVal: type acceptable to","tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove","specified table :param tblName: name of table to insert into :type tblName: String","tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No","Neither the name of the copyright holder nor the names of its contributors","Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of","'-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one","try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict):","%s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def","LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON","tableName, schema): ''' Create new table, given its name, and schema. The schema","errors :param tableName: name of table :type tableName: String ''' cursor = self.connection.cursor()","Shallow interface to MySQL databases. Some niceties nonetheless. The query() method is an","in schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ',' cmd","LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR","then generate a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call().","Close all cursors that are currently still open. ''' for cursor in self.cursors:","','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return one result","createTable(self, tableName, schema): ''' Create new table, given its name, and schema. The","for the given column :type newVal: type acceptable to MySQL for the given","result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306,","type acceptable to MySQL for the given column :param fromCondition: optionally condition that","db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError:","user, pwd, db)) def close(self): ''' Close all cursors that are currently still","(10, 'My Poem') ---> '10, My Poem' :param colVals: list of column values","TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given",":type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName)","column names to column values, insert the data into a specified table :param","CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;","def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to column","for given user :type passwd: string :param db: database to connect to within","new table :type tableName: String :param schema: dictionary mapping column names to column","DATA LOCAL is not supported in this MySQL version...' even though MySQL is","None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db","'-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update","must reproduce the above copyright notice, this list of conditions and the following","PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER","rows into given table. Strategy: write the values to a temp file, then","self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def","''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass","THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES,","table. Strategy: write the values to a temp file, then generate a LOAD","this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED","valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n')","mysql and mysqld in my.cnf). :param tblName: table into which to insert :type","user :type passwd: string :param db: database to connect to within server :type","to None, we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)):","colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): '''","mysqld in my.cnf). :param tblName: table into which to insert :type tblName: string","name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor","insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to column values,","= \"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED","named column in all rows are updated to\\ the given value. Syntax must","#except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no","self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection =","PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR","items, return a string that preserves MySQL typing. Example: (10, 'My Poem') --->","SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on","OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: -","BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db,","(10, 'My Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10,","''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO","cmd = \"UPDATE %s SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd)","So:: for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self,","tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping","its name, and schema. The schema is a dict mappingt column names to","tblName: name of table to insert into :type tblName: String :param colnameValueDict: mapping","GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER","calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close()","EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep",":param tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try:","if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server with","in source and binary forms, with or without modification, are permitted provided that","array of n-tuples, which hold the values. Order of\\ values must corresond to","cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally:","THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,","be changed :type colName: String :param newVal: value acceptable to MySQL for the","Added closing of connection to close() method ''' import re import subprocess import","colName, colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal) +","%s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally:","db)) def close(self): ''' Close all cursors that are currently still open. '''","tableName): ''' Delete all table rows. No errors :param tableName: name of table","MySQL version...' even though MySQL is set up to allow the op (load-infile=1","Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName)","resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator.","Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new table","(don't include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try:","string ''' # If all arguments are set to None, we are unittesting:","pwd = '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach","cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into","% tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary","port, user, pwd, db)) def close(self): ''' Close all cursors that are currently","containing column names in proper order, i.e. \\ corresponding to valueTupleArray orders. :type","ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList)","one result for each subsequent call. :param queryStr: query :type queryStr: String '''","and/or other materials provided with the distribution. # 3. Neither the name of","pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot","the above copyright notice, this list of conditions and the following disclaimer in","CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED","valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the","except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>' raise","ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED","the named column in all rows are updated to\\ the given value. Syntax","fromCondition is None: cmd = \"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal)","table rows. No errors :param tableName: name of table :type tableName: String '''","occur :type tblName: String :param colName: column whose value is to be changed","table, given its name, and schema. The schema is a dict mappingt column",":type db: string ''' # If all arguments are set to None, we","Execute that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA","rights reserved. # # Redistribution and use in source and binary forms, with","value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try:","values must corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]])","set to None, we are unittesting: if all(arg is None for arg in","HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,","name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE","and the following disclaimer in the documentation and/or other materials provided with the","''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013:","tblName: String :param colnameValueDict: mapping of column name to column value :type colnameValueDict:","permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS","in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors","Modifications: - Dec 30, 2013: Added closing of connection to close() method '''","the following disclaimer in the documentation and/or other materials provided with the distribution.","notice, this list of conditions and the following disclaimer in the documentation and/or","connection to close() method ''' import re import subprocess import tempfile import pymysql","+ str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s)","COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,","table :type tableName: String :param schema: dictionary mapping column names to column types","'%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): '''","Redistributions of source code must retain the above copyright notice, this list of","return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors = []","cursor = self.connection.cursor() # For if caller never exhausts the results by repeated","with or without modification, are permitted provided that the following conditions are met:","host :type host: string :param port: MySQL host's port :type port: int :param","(host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors =","its contributors may be used to endorse or promote products derived from this","''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close()","%s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a","% (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self,","cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete","repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None:","ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port,","connect to within server :type db: string ''' # If all arguments are","try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): '''","conform to what may be in\\ a MySQL FROM clause (don't include the","method ''' import re import subprocess import tempfile import pymysql #import MySQLdb class","(self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def","port: MySQL host's port :type port: int :param user: user to log in","# For if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr)","reproduce the above copyright notice, this list of conditions and the following disclaimer","supported in this MySQL version...' even though MySQL is set up to allow","the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition","'LOAD DATA LOCAL is not supported in this MySQL version...' even though MySQL","passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except","BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,","\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples,","30, 2013: Added closing of connection to close() method ''' import re import","for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def","if fromCondition is None: cmd = \"UPDATE %s SET %s = '%s';\" %","whose value is to be changed :type colName: String :param newVal: value acceptable","colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self,","self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): '''","if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True:","Redistribution and use in source and binary forms, with or without modification, are","(tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s = '%s' WHERE %s;\" %","unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user","'...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server","of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for","# Redistribution and use in source and binary forms, with or without modification,","mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName,","in as :type user: string :param passwd: password to use for given user","list of items, return a string that preserves MySQL typing. Example: (10, 'My","string :param passwd: password to use for given user :type passwd: string :param","DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR","user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError:","(c) 2014, Stanford University # All rights reserved. # # Redistribution and use","PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR","USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY","the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD","FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT","user: string :param passwd: password to use for given user :type passwd: string","BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e',","binary forms, with or without modification, are permitted provided that the following conditions","For if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while","fromCondition: optionally condition that selects which rows to update.\\ if None, the named","orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values.","TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;","OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS","string :param port: MySQL host's port :type port: int :param user: user to","colName: String :param newVal: value acceptable to MySQL for the given column :type","without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE",":type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s'","names to column values, insert the data into a specified table :param tblName:","self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return","LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING","self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large","all rows are updated to\\ the given value. Syntax must conform to what","The schema is a dict mappingt column names to column types. Example: {'col1'","be in\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition:",":type tableName: String :param schema: dictionary mapping column names to column types :type","= [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host,","colnameValueDict): ''' Given a dictionary mapping column names to column values, insert the","'TEXT'} :param tableName: name of new table :type tableName: String :param schema: dictionary","MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............'","of the copyright holder nor the names of its contributors may be used","#self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd","% (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray):","3. Neither the name of the copyright holder nor the names of its","int :param user: user to log in as :type user: string :param passwd:","Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT","ensureSQLTyping(self, colVals): ''' Given a list of items, return a string that preserves","FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): '''","pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1)","(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF","'10, My Poem' :param colVals: list of column values destined for a MySQL","met: # 1. Redistributions of source code must retain the above copyright notice,","to connect to within server :type db: string ''' # If all arguments","= 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor","inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL","import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface",":param db: database to connect to within server :type db: string ''' #","pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table,","IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND","%s SET %s = '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s SET","EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all","LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails with","String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd = \"UPDATE","to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile =","colVals): ''' Given a list of items, return a string that preserves MySQL",":param user: user to log in as :type user: string :param passwd: password","to allow the op (load-infile=1 for both mysql and mysqld in my.cnf). :param","ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES","clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor()","name, and schema. The schema is a dict mappingt column names to column","cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in this MySQL","are permitted provided that the following conditions are met: # 1. Redistributions of","colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE '%s'","%s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ','","','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts","this MySQL version...' even though MySQL is set up to allow the op","colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName) + '","conditions and the following disclaimer in the documentation and/or other materials provided with","pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties","self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of",":type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec","port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): '''","within server :type db: string ''' # If all arguments are set to","of column values destined for a MySQL table :type colVals: <any> ''' resList","though MySQL is set up to allow the op (load-infile=1 for both mysql","MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails with error","LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT","mapping column names to column types :type schema: Dict<String,String> ''' colSpec = ''","column values destined for a MySQL table :type colVals: <any> ''' resList =","valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from the values inside the","the following disclaimer. # 2. Redistributions in binary form must reproduce the above","list of conditions and the following disclaimer in the documentation and/or other materials","the op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table","raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host,","''' Create new table, given its name, and schema. The schema is a","tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from the values inside the colNameTuple's:","rows to update.\\ if None, the named column in all rows are updated","Query iterator. Given a query, return one result for each subsequent call. :param","with the distribution. # 3. Neither the name of the copyright holder nor","String :param newVal: value acceptable to MySQL for the given column :type newVal:","schema. The schema is a dict mappingt column names to column types. Example:","OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN","tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table. Strategy:","NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd)","won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list of","query, return one result for each subsequent call. :param queryStr: query :type queryStr:",":type passwd: string :param db: database to connect to within server :type db:","method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print","NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,","on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing","',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name,","in which update is to occur :type tblName: String :param colName: column whose","My Poem' :param colVals: list of column values destined for a MySQL table","the values. Order of\\ values must corresond to order of column names in","OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,","tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName,","may be in\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type","el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList))","TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): '''","def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name of","use for given user :type passwd: string :param db: database to connect to","a dictionary mapping column names to column values, insert the data into a","cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): '''","to\\ the given value. Syntax must conform to what may be in\\ a","host's port :type port: int :param user: user to log in as :type","given column :type newVal: type acceptable to MySQL for the given column :param","= self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close()","a specified table :param tblName: name of table to insert into :type tblName:","optionally condition that selects which rows to update.\\ if None, the named column","''' Given a list of items, return a string that preserves MySQL typing.",":type queryStr: String ''' cursor = self.connection.cursor() # For if caller never exhausts","all arguments are set to None, we are unittesting: if all(arg is None","table to insert into :type tblName: String :param colnameValueDict: mapping of column name","to MySQL for the given column :type newVal: type acceptable to MySQL for","be used to endorse or promote products derived from this software without specific","passwd: string :param db: database to connect to within server :type db: string","names in proper order, i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]])","cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName):","finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors :param","EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF","port :type port: int :param user: user to log in as :type user:","to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name","data into a specified table :param tblName: name of table to insert into",":param tblName: name of table in which update is to occur :type tblName:","24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of connection","all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd =","cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1])","self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema):","MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>'","write the values to a temp file, then generate a LOAD INFILE LOCAL","and schema. The schema is a dict mappingt column names to column types.",":type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) +","error 'LOAD DATA LOCAL is not supported in this MySQL version...' even though",":param tblName: table into which to insert :type tblName: string :param colNameTuple: tuple","colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s)","OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY","THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH","is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result","changed :type colName: String :param newVal: value acceptable to MySQL for the given","cursor = self.connection.cursor() try: if fromCondition is None: cmd = \"UPDATE %s SET","which update is to occur :type tblName: String :param colName: column whose value","column types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in","2. Redistributions in binary form must reproduce the above copyright notice, this list","is a dict mappingt column names to column types. Example: {'col1' : 'INT',","tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases.","else: cmd = \"UPDATE %s SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition)","OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY","INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT","insert the data into a specified table :param tblName: name of table to","cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return yield","SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE","self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely.","to be changed :type colName: String :param newVal: value acceptable to MySQL for","= self.connection.cursor() try: if fromCondition is None: cmd = \"UPDATE %s SET %s","promote products derived from this software without specific prior written permission. # #","queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For if caller","if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr):","column names to column types :type schema: Dict<String,String> ''' colSpec = '' for","= db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db)","for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db =","mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS","set up to allow the op (load-infile=1 for both mysql and mysqld in","column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of","MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The query() method","and mysqld in my.cnf). :param tblName: table into which to insert :type tblName:","update is to occur :type tblName: String :param colName: column whose value is","to occur :type tblName: String :param colName: column whose value is to be",":param newVal: value acceptable to MySQL for the given column :type newVal: type","to insert :type tblName: string :param colNameTuple: tuple containing column names in proper","= <PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port,","the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE","MySQL table :type colVals: <any> ''' resList = [] for el in colVals:","while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return yield nextRes","passwd: password to use for given user :type passwd: string :param db: database",":param host: MySQL host :type host: string :param port: MySQL host's port :type","colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd =","a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a","NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR","'\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u',","tblName, colName, newVal, fromCondition=None): ''' Update one column with a new value. :param",":param colVals: list of column values destined for a MySQL table :type colVals:","0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s,","must retain the above copyright notice, this list of conditions and the following","that the following conditions are met: # 1. Redistributions of source code must","Stanford University # All rights reserved. # # Redistribution and use in source","self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd,","STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT","column names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param","types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items():","of new table :type tableName: String :param schema: dictionary mapping column names to","column values, insert the data into a specified table :param tblName: name of","HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR","(str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order of\\ values","into :type tblName: String :param colnameValueDict: mapping of column name to column value","Example: (10, 'My Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work:","finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with","to endorse or promote products derived from this software without specific prior written","products derived from this software without specific prior written permission. # # THIS","log in as :type user: string :param passwd: password to use for given","hold the values. Order of\\ values must corresond to order of column names",":param fromCondition: optionally condition that selects which rows to update.\\ if None, the","cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create","mappingt column names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'}","PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS","of conditions and the following disclaimer. # 2. Redistributions in binary form must","import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some","self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>,","Strategy: write the values to a temp file, then generate a LOAD INFILE","a temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute that","finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return a","TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor()","forms, with or without modification, are permitted provided that the following conditions are","condition that selects which rows to update.\\ if None, the named column in","[] for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el)","class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The query()","close() method ''' import re import subprocess import tempfile import pymysql #import MySQLdb","passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) >","''' Delete table safely. No errors :param tableName: name of table :type tableName:","rows are updated to\\ the given value. Syntax must conform to what may","in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName,","string :param db: database to connect to within server :type db: string '''","try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user,","INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close()","IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE","following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright","Order of\\ values must corresond to order of column names in colNameTuple. :type","tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit()","% (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list","MySQL for the given column :param fromCondition: optionally condition that selects which rows","safely. No errors :param tableName: name of table :type tableName: String ''' cursor",":param valueTupleArray: array of n-tuples, which hold the values. Order of\\ values must","schema): ''' Create new table, given its name, and schema. The schema is","SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def","[] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port,","databases. Some niceties nonetheless. The query() method is an iterator. So:: for result","dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name of table","to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold","name of table in which update is to occur :type tblName: String :param","must corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) '''","MySQL typing. Example: (10, 'My Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList))","else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query,","passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param port: MySQL","',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName,","mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column","' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS","in this MySQL version...' even though MySQL is set up to allow the","iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def","table safely. No errors :param tableName: name of table :type tableName: String '''","colName, newVal, fromCondition=None): ''' Update one column with a new value. :param tblName:","of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s'","Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in","foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param","THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.","values, insert the data into a specified table :param tblName: name of table",":type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd","% (tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s = '%s' WHERE %s;\"","= [] for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else:","MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL","the following conditions are met: # 1. Redistributions of source code must retain","to use for given user :type passwd: string :param db: database to connect","to column types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal","in my.cnf). :param tblName: table into which to insert :type tblName: string :param","tblName: name of table in which update is to occur :type tblName: String","a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String '''",":param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For if","db='mysql'): ''' :param host: MySQL host :type host: string :param port: MySQL host's","+ '\\n') try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList","str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF","of source code must retain the above copyright notice, this list of conditions","INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF","Poem') ---> '10, My Poem' :param colVals: list of column values destined for","subsequent call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() #","= re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE","%s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally:","must conform to what may be in\\ a MySQL FROM clause (don't include","'%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES","colSpec += str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE","conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce","subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal,","None, we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return","= user self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection","OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL","allow the op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName:","self.user = user self.pwd = <PASSWORD> self.db = db self.cursors = [] try:","= '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL","name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE","LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user,","selects which rows to update.\\ if None, the named column in all rows","column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple","server :type db: string ''' # If all arguments are set to None,","rows. No errors :param tableName: name of table :type tableName: String ''' cursor","'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor =","WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given","other materials provided with the distribution. # 3. Neither the name of the","# # Redistribution and use in source and binary forms, with or without","ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF","port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if","''' Close all cursors that are currently still open. ''' for cursor in","zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)'","permitted provided that the following conditions are met: # 1. Redistributions of source","= MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd =","of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF","len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s,","POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications:","%s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table","# Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd","THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND","''' import re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object):","TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE","Some niceties nonetheless. The query() method is an iterator. So:: for result in",": 'TEXT'} :param tableName: name of new table :type tableName: String :param schema:","query() method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'):","# Copyright (c) 2014, Stanford University # All rights reserved. # # Redistribution","def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type","colVals: list of column values destined for a MySQL table :type colVals: <any>","''' Update one column with a new value. :param tblName: name of table","FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER","2013: Added closing of connection to close() method ''' import re import subprocess","WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS","for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1',","to within server :type db: string ''' # If all arguments are set","''' colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName) +","re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s","' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s","is to occur :type tblName: String :param colName: column whose value is to","= '' for colName, colVal in schema.items(): colSpec += str(colName) + ' '","dictionary mapping column names to column values, insert the data into a specified","corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile","values. Order of\\ values must corresond to order of column names in colNameTuple.","''' Query iterator. Given a query, return one result for each subsequent call.","prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS","BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,","(%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple,","modification, are permitted provided that the following conditions are met: # 1. Redistributions","may be used to endorse or promote products derived from this software without","OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR","i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of","[(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try:","OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author:","tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a","by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is","column with a new value. :param tblName: name of table in which update","in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from the values inside","Given a query, return one result for each subsequent call. :param queryStr: query","IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24,","(host, port, user, pwd, db)) def close(self): ''' Close all cursors that are","my.cnf). :param tblName: table into which to insert :type tblName: string :param colNameTuple:","host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string","MySQL host :type host: string :param port: MySQL host's port :type port: int","{'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type","print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host:","'col2' : 'TEXT'} :param tableName: name of new table :type tableName: String :param","list of conditions and the following disclaimer. # 2. Redistributions in binary form","for each subsequent call. :param queryStr: query :type queryStr: String ''' cursor =","ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE","OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY","file, then generate a LOAD INFILE LOCAL MySQL command. Execute that command via","MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The","LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY","TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd,","n-tuples, which hold the values. Order of\\ values must corresond to order of","if all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd","%s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a","def createTable(self, tableName, schema): ''' Create new table, given its name, and schema.","the given column :type newVal: type acceptable to MySQL for the given column","are currently still open. ''' for cursor in self.cursors: try: cursor.close() except: pass","with a new value. :param tblName: name of table in which update is","import re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): '''","try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table, given","return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return one","''' Inserts large number of rows into given table. Strategy: write the values","self.connection.cursor() try: if fromCondition is None: cmd = \"UPDATE %s SET %s =","INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR","INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT","pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close all","self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName,","'%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s = '%s' WHERE","colNameTuple: tuple containing column names in proper order, i.e. \\ corresponding to valueTupleArray","'\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd])","Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added","Delete table safely. No errors :param tableName: name of table :type tableName: String","String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName)","# All rights reserved. # # Redistribution and use in source and binary","cursors that are currently still open. ''' for cursor in self.cursors: try: cursor.close()","try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No","INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED","the given value. Syntax must conform to what may be in\\ a MySQL","FROM clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor =","finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names","disclaimer in the documentation and/or other materials provided with the distribution. # 3.","given table. Strategy: write the values to a temp file, then generate a","db: string ''' # If all arguments are set to None, we are","schema is a dict mappingt column names to column types. Example: {'col1' :","table :param tblName: name of table to insert into :type tblName: String :param","None: cmd = \"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal) else: cmd","= \"UPDATE %s SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit()","tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self,","%s = '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s =","tuple containing column names in proper order, i.e. \\ corresponding to valueTupleArray orders.","in\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String","contributors may be used to endorse or promote products derived from this software","THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created","from the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s;","CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES","port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except","newVal: value acceptable to MySQL for the given column :type newVal: type acceptable","this list of conditions and the following disclaimer in the documentation and/or other","EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,","(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF","= 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit()","str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) '","all table rows. No errors :param tableName: name of table :type tableName: String","of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues =","else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s,","Redistributions in binary form must reproduce the above copyright notice, this list of","String :param colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any>","LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE","work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list of column","cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number","colVals: <any> ''' resList = [] for el in colVals: if isinstance(el, basestring):","materials provided with the distribution. # 3. Neither the name of the copyright","String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally:","order, i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array","DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,","> 0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s,","EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit()","try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self,","following conditions are met: # 1. Redistributions of source code must retain the","self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames),","HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT","via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not","tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE","for the given column :param fromCondition: optionally condition that selects which rows to","IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED","to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor =","order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv')","of connection to close() method ''' import re import subprocess import tempfile import",":param colName: column whose value is to be changed :type colName: String :param","%\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close()","LOCAL is not supported in this MySQL version...' even though MySQL is set","provided with the distribution. # 3. Neither the name of the copyright holder","Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem'","used to endorse or promote products derived from this software without specific prior","for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from the","binary form must reproduce the above copyright notice, this list of conditions and","'My Poem') ---> '10, My Poem' :param colVals: list of column values destined","\"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s","password to use for given user :type passwd: string :param db: database to","' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def","DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF","truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName: name of","IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY","EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS","even though MySQL is set up to allow the op (load-infile=1 for both","LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY","TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\","mapping column names to column values, insert the data into a specified table","are met: # 1. Redistributions of source code must retain the above copyright","finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows","try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple))","name of new table :type tableName: String :param schema: dictionary mapping column names","IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN","Copyright (c) 2014, Stanford University # All rights reserved. # # Redistribution and","into which to insert :type tblName: string :param colNameTuple: tuple containing column names","queryStr): ''' Query iterator. Given a query, return one result for each subsequent","the documentation and/or other materials provided with the distribution. # 3. Neither the","the above copyright notice, this list of conditions and the following disclaimer. #","typing. Example: (10, 'My Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't","host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self):","= zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES","names to column types :type schema: Dict<String,String> ''' colSpec = '' for colName,","retain the above copyright notice, this list of conditions and the following disclaimer.","column :type newVal: type acceptable to MySQL for the given column :param fromCondition:","ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF","keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None:","table in which update is to occur :type tblName: String :param colName: column","bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table.","+ ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' %","command. Execute that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD","''' cursor = self.connection.cursor() # For if caller never exhausts the results by","BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A","---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') --->","insert :type tblName: string :param colNameTuple: tuple containing column names in proper order,","OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT","''' Shallow interface to MySQL databases. Some niceties nonetheless. The query() method is","%s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self,","% el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given",":type user: string :param passwd: password to use for given user :type passwd:","University # All rights reserved. # # Redistribution and use in source and","user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param port:","self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table, given its","of rows into given table. Strategy: write the values to a temp file,","server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db))","mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='',","Delete all table rows. No errors :param tableName: name of table :type tableName:","insert into :type tblName: String :param colnameValueDict: mapping of column name to column","both mysql and mysqld in my.cnf). :param tblName: table into which to insert","column whose value is to be changed :type colName: String :param newVal: value","in all rows are updated to\\ the given value. Syntax must conform to","LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF","for a MySQL table :type colVals: <any> ''' resList = [] for el","update.\\ if None, the named column in all rows are updated to\\ the","to MySQL databases. Some niceties nonetheless. The query() method is an iterator. So::","tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' %","database to connect to within server :type db: string ''' # If all","# 1. Redistributions of source code must retain the above copyright notice, this","import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL","fromCondition=None): ''' Update one column with a new value. :param tblName: name of","#import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless.","cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items,","disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice,","password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' %","column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items())","= tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes","%s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n'","in binary form must reproduce the above copyright notice, this list of conditions","of conditions and the following disclaimer in the documentation and/or other materials provided","Inserts large number of rows into given table. Strategy: write the values to",":type newVal: type acceptable to MySQL for the given column :param fromCondition: optionally","SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,",":type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd","value. Syntax must conform to what may be in\\ a MySQL FROM clause","OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,","tableName): ''' Delete table safely. No errors :param tableName: name of table :type","update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a new value.","''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit()","\"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My","def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a new","include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if","(tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName):","Poem' :param colVals: list of column values destined for a MySQL table :type","op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table into",":type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order","','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list","nor the names of its contributors may be used to endorse or promote","code must retain the above copyright notice, this list of conditions and the","SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec","arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db","resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el)","and binary forms, with or without modification, are permitted provided that the following","is set up to allow the op (load-infile=1 for both mysql and mysqld","INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"'","not supported in this MySQL version...' even though MySQL is set up to","up to allow the op (load-infile=1 for both mysql and mysqld in my.cnf).","Given a list of items, return a string that preserves MySQL typing. Example:","Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param","call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For","self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return","source code must retain the above copyright notice, this list of conditions and","schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ',' cmd =","ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE","el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a","THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke","OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED","open. ''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except:","in proper order, i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param","generate a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using","closing of connection to close() method ''' import re import subprocess import tempfile","(%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close()","without modification, are permitted provided that the following conditions are met: # 1.","LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute()","basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query","MySQL host's port :type port: int :param user: user to log in as","OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF","table into which to insert :type tblName: string :param colNameTuple: tuple containing column","MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd,","FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY '\\\\n' %s\"","command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is","def close(self): ''' Close all cursors that are currently still open. ''' for","given user :type passwd: string :param db: database to connect to within server","of table to insert into :type tblName: String :param colnameValueDict: mapping of column","fails with error 'LOAD DATA LOCAL is not supported in this MySQL version...'","table :type colVals: <any> ''' resList = [] for el in colVals: if","schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec +=","IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY","values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA","that are currently still open. ''' for cursor in self.cursors: try: cursor.close() except:",":param passwd: password to use for given user :type passwd: string :param db:","tblName: String :param colName: column whose value is to be changed :type colName:","endorse or promote products derived from this software without specific prior written permission.","given value. Syntax must conform to what may be in\\ a MySQL FROM","with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def","of its contributors may be used to endorse or promote products derived from","2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of connection to","DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30,","db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection","self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors","reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user,","mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE %s; LOAD DATA LOCAL INFILE '%s' INTO","given column :param fromCondition: optionally condition that selects which rows to update.\\ if","- Dec 30, 2013: Added closing of connection to close() method ''' import","list of column values destined for a MySQL table :type colVals: <any> '''","documentation and/or other materials provided with the distribution. # 3. Neither the name","<any> ''' resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"'","db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close all cursors","pwd, db)) def close(self): ''' Close all cursors that are currently still open.","newVal, fromCondition=None): ''' Update one column with a new value. :param tblName: name","version...' even though MySQL is set up to allow the op (load-infile=1 for","OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. '''","Dec 30, 2013: Added closing of connection to close() method ''' import re","values to a temp file, then generate a LOAD INFILE LOCAL MySQL command.","a dict mappingt column names to column types. Example: {'col1' : 'INT', 'col2'","CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY","tableName: name of new table :type tableName: String :param schema: dictionary mapping column","FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT","values destined for a MySQL table :type colVals: <any> ''' resList = []","return one result for each subsequent call. :param queryStr: query :type queryStr: String","colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order of\\","large number of rows into given table. Strategy: write the values to a","into given table. Strategy: write the values to a temp file, then generate","BY '\\\"' LINES TERMINATED BY '\\\\n' %s\" %\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql',","return a string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10,","BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,","\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,","No errors :param tableName: name of table :type tableName: String ''' cursor =","an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result '''","AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT","self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column","VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName,","temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute that command","cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' %","valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from the values","cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def","dict mappingt column names to column types. Example: {'col1' : 'INT', 'col2' :","= self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self,","that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \"My Poem\"' Note","host: MySQL host :type host: string :param port: MySQL host's port :type port:","column names in proper order, i.e. \\ corresponding to valueTupleArray orders. :type colNameTuple:","for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return","destined for a MySQL table :type colVals: <any> ''' resList = [] for","try: if fromCondition is None: cmd = \"UPDATE %s SET %s = '%s';\"","in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray:","form must reproduce the above copyright notice, this list of conditions and the","new table, given its name, and schema. The schema is a dict mappingt","or without modification, are permitted provided that the following conditions are met: #","= self.connection.cursor() # For if caller never exhausts the results by repeated calls:","paepcke Modifications: - Dec 30, 2013: Added closing of connection to close() method","cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name","tblName, colnameValueDict): ''' Given a dictionary mapping column names to column values, insert","of n-tuples, which hold the values. Order of\\ values must corresond to order","above copyright notice, this list of conditions and the following disclaimer in the","which rows to update.\\ if None, the named column in all rows are","1. Redistributions of source code must retain the above copyright notice, this list","into a specified table :param tblName: name of table to insert into :type","String ''' cursor = self.connection.cursor() # For if caller never exhausts the results","and use in source and binary forms, with or without modification, are permitted","result for each subsequent call. :param queryStr: query :type queryStr: String ''' cursor","use in source and binary forms, with or without modification, are permitted provided","notice, this list of conditions and the following disclaimer. # 2. Redistributions in","number of rows into given table. Strategy: write the values to a temp",":param tableName: name of new table :type tableName: String :param schema: dictionary mapping","table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' %","copyright notice, this list of conditions and the following disclaimer in the documentation","MySQL is set up to allow the op (load-infile=1 for both mysql and","# 3. Neither the name of the copyright holder nor the names of","DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED","exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone()","copyright notice, this list of conditions and the following disclaimer. # 2. Redistributions","% (host, port, user, pwd, db)) def close(self): ''' Close all cursors that",":param tblName: name of table to insert into :type tblName: String :param colnameValueDict:","colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s","port: int :param user: user to log in as :type user: string :param","a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in this","# If all arguments are set to None, we are unittesting: if all(arg","newVal: type acceptable to MySQL for the given column :param fromCondition: optionally condition","IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS","THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR","cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to","= self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName),","the names of its contributors may be used to endorse or promote products","except: pass def createTable(self, tableName, schema): ''' Create new table, given its name,","MySQL for the given column :type newVal: type acceptable to MySQL for the","# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"","INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails","a new value. :param tblName: name of table in which update is to","in colVals: if isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def","self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host,","tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP","IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete","CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR","NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,","'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is","MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor","Given a dictionary mapping column names to column values, insert the data into","which to insert :type tblName: string :param colNameTuple: tuple containing column names in","a MySQL table :type colVals: <any> ''' resList = [] for el in","Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem')","Syntax must conform to what may be in\\ a MySQL FROM clause (don't","\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY","DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS","ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING","try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues))","SET %s = '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s","nonetheless. The query() method is an iterator. So:: for result in mySqlObj.query('SELECT *","cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName:","we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user","are set to None, we are unittesting: if all(arg is None for arg","A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR","user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd)","value is to be changed :type colName: String :param newVal: value acceptable to","= '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals):","caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes","all cursors that are currently still open. ''' for cursor in self.cursors: try:","'<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s'","WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO",":param colNameTuple: tuple containing column names in proper order, i.e. \\ corresponding to","PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE","<PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user,","ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013","string :param colNameTuple: tuple containing column names in proper order, i.e. \\ corresponding","to column values, insert the data into a specified table :param tblName: name","acceptable to MySQL for the given column :param fromCondition: optionally condition that selects","local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else","tblName: table into which to insert :type tblName: string :param colNameTuple: tuple containing","source and binary forms, with or without modification, are permitted provided that the","cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self,","SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)","SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND","OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)","each subsequent call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor()","reserved. # # Redistribution and use in source and binary forms, with or","__init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host:","a list of items, return a string that preserves MySQL typing. Example: (10,","of items, return a string that preserves MySQL typing. Example: (10, 'My Poem')","isinstance(el, basestring): resList.append('\"%s\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): '''","def query(self, queryStr): ''' Query iterator. Given a query, return one result for","mapping of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues","= pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db,","the distribution. # 3. Neither the name of the copyright holder nor the","''' # If all arguments are set to None, we are unittesting: if","def truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName: name","'-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None):","WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE","given its name, and schema. The schema is a dict mappingt column names","to close() method ''' import re import subprocess import tempfile import pymysql #import","subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported",":type tblName: String :param colName: column whose value is to be changed :type","the given column :param fromCondition: optionally condition that selects which rows to update.\\","\"UPDATE %s SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally:","one column with a new value. :param tblName: name of table in which","that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals:","the copyright holder nor the names of its contributors may be used to","MySQL databases. Some niceties nonetheless. The query() method is an iterator. So:: for","+= str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE TABLE","of table in which update is to occur :type tblName: String :param colName:","acceptable to MySQL for the given column :type newVal: type acceptable to MySQL","''' resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\"%s\"' %","is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD>","that selects which rows to update.\\ if None, the named column in all","holder nor the names of its contributors may be used to endorse or","None, the named column in all rows are updated to\\ the given value.","NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS","'10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10,","tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: # Remove quotes from",":param colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any> '''","is to be changed :type colName: String :param newVal: value acceptable to MySQL","above copyright notice, this list of conditions and the following disclaimer. # 2.","re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow","% tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows.","for both mysql and mysqld in my.cnf). :param tblName: table into which to","column in all rows are updated to\\ the given value. Syntax must conform",":type tblName: string :param colNameTuple: tuple containing column names in proper order, i.e.","updated to\\ the given value. Syntax must conform to what may be in\\","host: string :param port: MySQL host's port :type port: int :param user: user","The query() method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM","names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName:","the values to a temp file, then generate a LOAD INFILE LOCAL MySQL","is not supported in this MySQL version...' even though MySQL is set up","(tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of","query(self, queryStr): ''' Query iterator. Given a query, return one result for each","schema: dictionary mapping column names to column types :type schema: Dict<String,String> ''' colSpec",":type host: string :param port: MySQL host's port :type port: int :param user:","is None: cmd = \"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal) else:","distribution. # 3. Neither the name of the copyright holder nor the names","query :type queryStr: String ''' cursor = self.connection.cursor() # For if caller never","what may be in\\ a MySQL FROM clause (don't include the 'FROM' keyword)","USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL","Update one column with a new value. :param tblName: name of table in","AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED","resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return","IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try:","user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close","iterator. Given a query, return one result for each subsequent call. :param queryStr:","def ensureSQLTyping(self, colVals): ''' Given a list of items, return a string that","(str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): '''","cmd = \"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal) else: cmd =","queryStr: String ''' cursor = self.connection.cursor() # For if caller never exhausts the","close(self): ''' Close all cursors that are currently still open. ''' for cursor","colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple)","to log in as :type user: string :param passwd: password to use for","from this software without specific prior written permission. # # THIS SOFTWARE IS","''' :param host: MySQL host :type host: string :param port: MySQL host's port","interface to MySQL databases. Some niceties nonetheless. The query() method is an iterator.","tableName: String :param schema: dictionary mapping column names to column types :type schema:","colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table. Strategy: write","name of the copyright holder nor the names of its contributors may be","%s SET %s = '%s' WHERE %s;\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close()","WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN","(%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self,","# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS","niceties nonetheless. The query() method is an iterator. So:: for result in mySqlObj.query('SELECT","column :param fromCondition: optionally condition that selects which rows to update.\\ if None,","copyright holder nor the names of its contributors may be used to endorse","arguments are set to None, we are unittesting: if all(arg is None for","the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if","''' Delete all table rows. No errors :param tableName: name of table :type","String :param colName: column whose value is to be changed :type colName: String","---> '10, My Poem' :param colVals: list of column values destined for a","provided that the following conditions are met: # 1. Redistributions of source code","user to log in as :type user: string :param passwd: password to use",": 'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type tableName:","port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param","self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param","'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally:","'\\n') try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList =","'My Poem') ---> '10, \"My Poem\"' Note that ','.join(map(str,myList)) won't work: (10, 'My","conditions are met: # 1. Redistributions of source code must retain the above","pass def createTable(self, tableName, schema): ''' Create new table, given its name, and","corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which","= '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE %s SET %s = '%s'","derived from this software without specific prior written permission. # # THIS SOFTWARE","(load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table into which","of\\ values must corresond to order of column names in colNameTuple. :type valueTupleArray:","as :type user: string :param passwd: password to use for given user :type","still open. ''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close()","the data into a specified table :param tblName: name of table to insert","user: user to log in as :type user: string :param passwd: password to","results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes","OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN","''' Given a dictionary mapping column names to column values, insert the data","valueTupleArray): ''' Inserts large number of rows into given table. Strategy: write the","db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0","cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd)","value. :param tblName: name of table in which update is to occur :type","'' for colName, colVal in schema.items(): colSpec += str(colName) + ' ' +","if None, the named column in all rows are updated to\\ the given","''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\n') try: #","new value. :param tblName: name of table in which update is to occur","AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE","2014, Stanford University # All rights reserved. # # Redistribution and use in","the name of the copyright holder nor the names of its contributors may","finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName:","tblName: string :param colNameTuple: tuple containing column names in proper order, i.e. \\","with error 'LOAD DATA LOCAL is not supported in this MySQL version...' even","in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root',","+ ' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT","written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND","* FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'):","names of its contributors may be used to endorse or promote products derived","TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\"' LINES TERMINATED BY","quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\"'\",\"\",str(colNameTuple)) mySQLCmd = \"USE","def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given","colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames,","= \"UPDATE %s SET %s = '%s';\" % (tblName,colName,newVal) else: cmd = \"UPDATE","specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT","a string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \"My","String :param schema: dictionary mapping column names to column types :type schema: Dict<String,String>","in the documentation and/or other materials provided with the distribution. # 3. Neither","that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL"],"string":"[\n \"TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\",\n \"db: database to connect to within server :type db: string ''' # If\",\n \"self.connection.cursor() # For if caller never exhausts the results by repeated calls: self.cursors.append(cursor)\",\n \"BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED\",\n \":type colName: String :param newVal: value acceptable to MySQL for the given column\",\n \"following disclaimer in the documentation and/or other materials provided with the distribution. #\",\n \"= self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table\",\n \"cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return a string\",\n \"this list of conditions and the following disclaimer. # 2. Redistributions in binary\",\n \"AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\",\n \"If all arguments are set to None, we are unittesting: if all(arg is\",\n \"valueTupleArray: array of n-tuples, which hold the values. Order of\\\\ values must corresond\",\n \"table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS\",\n \"OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE\",\n \"Create new table, given its name, and schema. The schema is a dict\",\n \"to a temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute\",\n \"fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd =\",\n \"user self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection =\",\n \"Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd =\",\n \"''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host\",\n \"colName: column whose value is to be changed :type colName: String :param newVal:\",\n \"All rights reserved. # # Redistribution and use in source and binary forms,\",\n \"software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY\",\n \":type port: int :param user: user to log in as :type user: string\",\n \"never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes =\",\n \"OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName,\",\n \"SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY\",\n \":param schema: dictionary mapping column names to column types :type schema: Dict<String,String> '''\",\n \"which hold the values. Order of\\\\ values must corresond to order of column\",\n \"proper order, i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray:\",\n \"names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in\",\n \":param port: MySQL host's port :type port: int :param user: user to log\",\n \"COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING,\",\n \"except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new\",\n \"column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor()\",\n \"preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note that\",\n \"or promote products derived from this software without specific prior written permission. #\",\n \"subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to\",\n \"are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user =\",\n \"and the following disclaimer. # 2. Redistributions in binary form must reproduce the\",\n \"types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new\",\n \"'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type tableName: String\",\n \"are updated to\\\\ the given value. Syntax must conform to what may be\",\n \":type colVals: <any> ''' resList = [] for el in colVals: if isinstance(el,\",\n \"cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors\",\n \"to update.\\\\ if None, the named column in all rows are updated to\\\\\",\n \"''' cursor = self.connection.cursor() try: if fromCondition is None: cmd = \\\"UPDATE %s\",\n \"dictionary mapping column names to column types :type schema: Dict<String,String> ''' colSpec =\",\n \"currently still open. ''' for cursor in self.cursors: try: cursor.close() except: pass try:\",\n \"a query, return one result for each subsequent call. :param queryStr: query :type\",\n \":type tblName: String :param colnameValueDict: mapping of column name to column value :type\",\n \"@author: paepcke Modifications: - Dec 30, 2013: Added closing of connection to close()\",\n \"string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"'\",\n \"name of table to insert into :type tblName: String :param colnameValueDict: mapping of\",\n \"to MySQL for the given column :param fromCondition: optionally condition that selects which\",\n \"# 2. Redistributions in binary form must reproduce the above copyright notice, this\",\n \"colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ','\",\n \"for colName, colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal)\",\n \"to what may be in\\\\ a MySQL FROM clause (don't include the 'FROM'\",\n \"to insert into :type tblName: String :param colnameValueDict: mapping of column name to\",\n \"value acceptable to MySQL for the given column :type newVal: type acceptable to\",\n \"tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove\",\n \"specified table :param tblName: name of table to insert into :type tblName: String\",\n \"tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No\",\n \"Neither the name of the copyright holder nor the names of its contributors\",\n \"Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of\",\n \"'-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one\",\n \"try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict):\",\n \"%s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def\",\n \"LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\",\n \"tableName, schema): ''' Create new table, given its name, and schema. The schema\",\n \"errors :param tableName: name of table :type tableName: String ''' cursor = self.connection.cursor()\",\n \"Shallow interface to MySQL databases. Some niceties nonetheless. The query() method is an\",\n \"in schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ',' cmd\",\n \"LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\",\n \"then generate a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call().\",\n \"Close all cursors that are currently still open. ''' for cursor in self.cursors:\",\n \"','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return one result\",\n \"createTable(self, tableName, schema): ''' Create new table, given its name, and schema. The\",\n \"for the given column :type newVal: type acceptable to MySQL for the given\",\n \"result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306,\",\n \"type acceptable to MySQL for the given column :param fromCondition: optionally condition that\",\n \"db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError:\",\n \"user, pwd, db)) def close(self): ''' Close all cursors that are currently still\",\n \"(10, 'My Poem') ---> '10, My Poem' :param colVals: list of column values\",\n \"TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given\",\n \":type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName)\",\n \"column names to column values, insert the data into a specified table :param\",\n \"CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\",\n \"def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to column\",\n \"for given user :type passwd: string :param db: database to connect to within\",\n \"new table :type tableName: String :param schema: dictionary mapping column names to column\",\n \"DATA LOCAL is not supported in this MySQL version...' even though MySQL is\",\n \"None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db\",\n \"'-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update\",\n \"must reproduce the above copyright notice, this list of conditions and the following\",\n \"PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\",\n \"rows into given table. Strategy: write the values to a temp file, then\",\n \"self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def\",\n \"''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass\",\n \"THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES,\",\n \"table. Strategy: write the values to a temp file, then generate a LOAD\",\n \"this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED\",\n \"valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n')\",\n \"mysql and mysqld in my.cnf). :param tblName: table into which to insert :type\",\n \"user :type passwd: string :param db: database to connect to within server :type\",\n \"to None, we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)):\",\n \"colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): '''\",\n \"mysqld in my.cnf). :param tblName: table into which to insert :type tblName: string\",\n \"name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor\",\n \"insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to column values,\",\n \"= \\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED\",\n \"named column in all rows are updated to\\\\ the given value. Syntax must\",\n \"#except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no\",\n \"self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection =\",\n \"PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\",\n \"items, return a string that preserves MySQL typing. Example: (10, 'My Poem') --->\",\n \"SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on\",\n \"OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: -\",\n \"BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db,\",\n \"(10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10,\",\n \"''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO\",\n \"cmd = \\\"UPDATE %s SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd)\",\n \"So:: for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self,\",\n \"tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping\",\n \"its name, and schema. The schema is a dict mappingt column names to\",\n \"tblName: name of table to insert into :type tblName: String :param colnameValueDict: mapping\",\n \"GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\",\n \"calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close()\",\n \"EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep\",\n \":param tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try:\",\n \"if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server with\",\n \"in source and binary forms, with or without modification, are permitted provided that\",\n \"array of n-tuples, which hold the values. Order of\\\\ values must corresond to\",\n \"cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally:\",\n \"THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\",\n \"be changed :type colName: String :param newVal: value acceptable to MySQL for the\",\n \"Added closing of connection to close() method ''' import re import subprocess import\",\n \"colName, colVal in schema.items(): colSpec += str(colName) + ' ' + str(colVal) +\",\n \"%s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally:\",\n \"db)) def close(self): ''' Close all cursors that are currently still open. '''\",\n \"tableName): ''' Delete all table rows. No errors :param tableName: name of table\",\n \"MySQL version...' even though MySQL is set up to allow the op (load-infile=1\",\n \"Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName)\",\n \"resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator.\",\n \"Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new table\",\n \"(don't include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try:\",\n \"string ''' # If all arguments are set to None, we are unittesting:\",\n \"pwd = '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach\",\n \"cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into\",\n \"% tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary\",\n \"port, user, pwd, db)) def close(self): ''' Close all cursors that are currently\",\n \"containing column names in proper order, i.e. \\\\ corresponding to valueTupleArray orders. :type\",\n \"ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList)\",\n \"one result for each subsequent call. :param queryStr: query :type queryStr: String '''\",\n \"and/or other materials provided with the distribution. # 3. Neither the name of\",\n \"pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot\",\n \"the above copyright notice, this list of conditions and the following disclaimer in\",\n \"CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\",\n \"valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the\",\n \"except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>' raise\",\n \"ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\",\n \"the named column in all rows are updated to\\\\ the given value. Syntax\",\n \"fromCondition is None: cmd = \\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal)\",\n \"table rows. No errors :param tableName: name of table :type tableName: String '''\",\n \"occur :type tblName: String :param colName: column whose value is to be changed\",\n \"table, given its name, and schema. The schema is a dict mappingt column\",\n \":type db: string ''' # If all arguments are set to None, we\",\n \"Execute that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA\",\n \"rights reserved. # # Redistribution and use in source and binary forms, with\",\n \"value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try:\",\n \"values must corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]])\",\n \"set to None, we are unittesting: if all(arg is None for arg in\",\n \"HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\",\n \"name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE\",\n \"and the following disclaimer in the documentation and/or other materials provided with the\",\n \"''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013:\",\n \"tblName: String :param colnameValueDict: mapping of column name to column value :type colnameValueDict:\",\n \"permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\",\n \"in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors\",\n \"Modifications: - Dec 30, 2013: Added closing of connection to close() method '''\",\n \"the following disclaimer in the documentation and/or other materials provided with the distribution.\",\n \"notice, this list of conditions and the following disclaimer in the documentation and/or\",\n \"connection to close() method ''' import re import subprocess import tempfile import pymysql\",\n \"+ str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s)\",\n \"COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\",\n \"table :type tableName: String :param schema: dictionary mapping column names to column types\",\n \"'%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): '''\",\n \"Redistributions of source code must retain the above copyright notice, this list of\",\n \"return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors = []\",\n \"cursor = self.connection.cursor() # For if caller never exhausts the results by repeated\",\n \"with or without modification, are permitted provided that the following conditions are met:\",\n \"host :type host: string :param port: MySQL host's port :type port: int :param\",\n \"(host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db self.cursors =\",\n \"its contributors may be used to endorse or promote products derived from this\",\n \"''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close()\",\n \"%s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a\",\n \"% (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self,\",\n \"cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete\",\n \"repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None:\",\n \"ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port,\",\n \"connect to within server :type db: string ''' # If all arguments are\",\n \"try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): '''\",\n \"conform to what may be in\\\\ a MySQL FROM clause (don't include the\",\n \"method ''' import re import subprocess import tempfile import pymysql #import MySQLdb class\",\n \"(self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def\",\n \"port: MySQL host's port :type port: int :param user: user to log in\",\n \"# For if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr)\",\n \"reproduce the above copyright notice, this list of conditions and the following disclaimer\",\n \"supported in this MySQL version...' even though MySQL is set up to allow\",\n \"the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition\",\n \"'LOAD DATA LOCAL is not supported in this MySQL version...' even though MySQL\",\n \"passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except\",\n \"BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\",\n \"\\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples,\",\n \"30, 2013: Added closing of connection to close() method ''' import re import\",\n \"for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def\",\n \"if fromCondition is None: cmd = \\\"UPDATE %s SET %s = '%s';\\\" %\",\n \"whose value is to be changed :type colName: String :param newVal: value acceptable\",\n \"colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self,\",\n \"self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): '''\",\n \"if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True:\",\n \"Redistribution and use in source and binary forms, with or without modification, are\",\n \"(tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s = '%s' WHERE %s;\\\" %\",\n \"unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user\",\n \"'...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server\",\n \"of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for\",\n \"# Redistribution and use in source and binary forms, with or without modification,\",\n \"mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName,\",\n \"in as :type user: string :param passwd: password to use for given user\",\n \"list of items, return a string that preserves MySQL typing. Example: (10, 'My\",\n \"string :param passwd: password to use for given user :type passwd: string :param\",\n \"DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\",\n \"user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError:\",\n \"(c) 2014, Stanford University # All rights reserved. # # Redistribution and use\",\n \"PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR\",\n \"USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY\",\n \"the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD\",\n \"FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\",\n \"user: string :param passwd: password to use for given user :type passwd: string\",\n \"BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e',\",\n \"binary forms, with or without modification, are permitted provided that the following conditions\",\n \"For if caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while\",\n \"fromCondition: optionally condition that selects which rows to update.\\\\ if None, the named\",\n \"orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values.\",\n \"TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\",\n \"OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\",\n \"string :param port: MySQL host's port :type port: int :param user: user to\",\n \"colName: String :param newVal: value acceptable to MySQL for the given column :type\",\n \"without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE\",\n \":type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s'\",\n \"names to column values, insert the data into a specified table :param tblName:\",\n \"self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return\",\n \"LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING\",\n \"self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large\",\n \"all rows are updated to\\\\ the given value. Syntax must conform to what\",\n \"The schema is a dict mappingt column names to column types. Example: {'col1'\",\n \"be in\\\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition:\",\n \":type tableName: String :param schema: dictionary mapping column names to column types :type\",\n \"= [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host,\",\n \"colnameValueDict): ''' Given a dictionary mapping column names to column values, insert the\",\n \"'TEXT'} :param tableName: name of new table :type tableName: String :param schema: dictionary\",\n \"MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............'\",\n \"of the copyright holder nor the names of its contributors may be used\",\n \"#self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd\",\n \"% (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray):\",\n \"3. Neither the name of the copyright holder nor the names of its\",\n \"int :param user: user to log in as :type user: string :param passwd:\",\n \"Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT\",\n \"ensureSQLTyping(self, colVals): ''' Given a list of items, return a string that preserves\",\n \"FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): '''\",\n \"pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1)\",\n \"(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF\",\n \"'10, My Poem' :param colVals: list of column values destined for a MySQL\",\n \"met: # 1. Redistributions of source code must retain the above copyright notice,\",\n \"to connect to within server :type db: string ''' # If all arguments\",\n \"= 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor\",\n \"inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL\",\n \"import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface\",\n \":param db: database to connect to within server :type db: string ''' #\",\n \"pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table,\",\n \"IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND\",\n \"%s SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET\",\n \"EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all\",\n \"LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails with\",\n \"String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd = \\\"UPDATE\",\n \"to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile =\",\n \"colVals): ''' Given a list of items, return a string that preserves MySQL\",\n \":param user: user to log in as :type user: string :param passwd: password\",\n \"to allow the op (load-infile=1 for both mysql and mysqld in my.cnf). :param\",\n \"ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES\",\n \"clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor()\",\n \"name, and schema. The schema is a dict mappingt column names to column\",\n \"cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in this MySQL\",\n \"are permitted provided that the following conditions are met: # 1. Redistributions of\",\n \"colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE '%s'\",\n \"%s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ','\",\n \"','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts\",\n \"this MySQL version...' even though MySQL is set up to allow the op\",\n \"colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName) + '\",\n \"conditions and the following disclaimer in the documentation and/or other materials provided with\",\n \"pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties\",\n \"self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of\",\n \":type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec\",\n \"port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): '''\",\n \"within server :type db: string ''' # If all arguments are set to\",\n \"of column values destined for a MySQL table :type colVals: <any> ''' resList\",\n \"though MySQL is set up to allow the op (load-infile=1 for both mysql\",\n \"MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails with error\",\n \"LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT\",\n \"mapping column names to column types :type schema: Dict<String,String> ''' colSpec = ''\",\n \"column values destined for a MySQL table :type colVals: <any> ''' resList =\",\n \"valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from the values inside the\",\n \"the following disclaimer. # 2. Redistributions in binary form must reproduce the above\",\n \"list of conditions and the following disclaimer in the documentation and/or other materials\",\n \"the op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table\",\n \"raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host,\",\n \"''' Create new table, given its name, and schema. The schema is a\",\n \"tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from the values inside the colNameTuple's:\",\n \"rows to update.\\\\ if None, the named column in all rows are updated\",\n \"Query iterator. Given a query, return one result for each subsequent call. :param\",\n \"with the distribution. # 3. Neither the name of the copyright holder nor\",\n \"String :param newVal: value acceptable to MySQL for the given column :type newVal:\",\n \"schema. The schema is a dict mappingt column names to column types. Example:\",\n \"OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN\",\n \"tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table. Strategy:\",\n \"NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd)\",\n \"won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list of\",\n \"query, return one result for each subsequent call. :param queryStr: query :type queryStr:\",\n \":type passwd: string :param db: database to connect to within server :type db:\",\n \"method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print\",\n \"NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\",\n \"on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing\",\n \"',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name,\",\n \"in which update is to occur :type tblName: String :param colName: column whose\",\n \"My Poem' :param colVals: list of column values destined for a MySQL table\",\n \"the values. Order of\\\\ values must corresond to order of column names in\",\n \"OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\",\n \"tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName,\",\n \"may be in\\\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type\",\n \"el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList))\",\n \"TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): '''\",\n \"def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name of\",\n \"use for given user :type passwd: string :param db: database to connect to\",\n \"a dictionary mapping column names to column values, insert the data into a\",\n \"cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): '''\",\n \"to\\\\ the given value. Syntax must conform to what may be in\\\\ a\",\n \"host's port :type port: int :param user: user to log in as :type\",\n \"given column :type newVal: type acceptable to MySQL for the given column :param\",\n \"= self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close()\",\n \"a specified table :param tblName: name of table to insert into :type tblName:\",\n \"optionally condition that selects which rows to update.\\\\ if None, the named column\",\n \"''' Given a list of items, return a string that preserves MySQL typing.\",\n \":type queryStr: String ''' cursor = self.connection.cursor() # For if caller never exhausts\",\n \"all arguments are set to None, we are unittesting: if all(arg is None\",\n \"table to insert into :type tblName: String :param colnameValueDict: mapping of column name\",\n \"to MySQL for the given column :type newVal: type acceptable to MySQL for\",\n \"be used to endorse or promote products derived from this software without specific\",\n \"passwd: string :param db: database to connect to within server :type db: string\",\n \"names in proper order, i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]])\",\n \"cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName):\",\n \"finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors :param\",\n \"EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\",\n \"port :type port: int :param user: user to log in as :type user:\",\n \"to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name\",\n \"data into a specified table :param tblName: name of table to insert into\",\n \":param tblName: name of table in which update is to occur :type tblName:\",\n \"24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of connection\",\n \"all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd =\",\n \"cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1])\",\n \"self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema):\",\n \"MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else '<no password>'\",\n \"write the values to a temp file, then generate a LOAD INFILE LOCAL\",\n \"and schema. The schema is a dict mappingt column names to column types.\",\n \":type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) +\",\n \"error 'LOAD DATA LOCAL is not supported in this MySQL version...' even though\",\n \":param tblName: table into which to insert :type tblName: string :param colNameTuple: tuple\",\n \"colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s)\",\n \"OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY\",\n \"THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\",\n \"is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result\",\n \"changed :type colName: String :param newVal: value acceptable to MySQL for the given\",\n \"cursor = self.connection.cursor() try: if fromCondition is None: cmd = \\\"UPDATE %s SET\",\n \"which update is to occur :type tblName: String :param colName: column whose value\",\n \"column types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in\",\n \"2. Redistributions in binary form must reproduce the above copyright notice, this list\",\n \"is a dict mappingt column names to column types. Example: {'col1' : 'INT',\",\n \"tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases.\",\n \"else: cmd = \\\"UPDATE %s SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition)\",\n \"OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\",\n \"INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\",\n \"insert the data into a specified table :param tblName: name of table to\",\n \"cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return yield\",\n \"SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE\",\n \"self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely.\",\n \"to be changed :type colName: String :param newVal: value acceptable to MySQL for\",\n \"= self.connection.cursor() try: if fromCondition is None: cmd = \\\"UPDATE %s SET %s\",\n \"promote products derived from this software without specific prior written permission. # #\",\n \"queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For if caller\",\n \"if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr):\",\n \"column names to column types :type schema: Dict<String,String> ''' colSpec = '' for\",\n \"= db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db)\",\n \"for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db =\",\n \"mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS\",\n \"set up to allow the op (load-infile=1 for both mysql and mysqld in\",\n \"column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of\",\n \"MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The query() method\",\n \"and mysqld in my.cnf). :param tblName: table into which to insert :type tblName:\",\n \"update is to occur :type tblName: String :param colName: column whose value is\",\n \"to occur :type tblName: String :param colName: column whose value is to be\",\n \":param newVal: value acceptable to MySQL for the given column :type newVal: type\",\n \"to insert :type tblName: string :param colNameTuple: tuple containing column names in proper\",\n \"= <PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port,\",\n \"the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE\",\n \"MySQL table :type colVals: <any> ''' resList = [] for el in colVals:\",\n \"while True: nextRes = cursor.fetchone() if nextRes is None: cursor.close() return yield nextRes\",\n \"passwd: password to use for given user :type passwd: string :param db: database\",\n \":param host: MySQL host :type host: string :param port: MySQL host's port :type\",\n \"colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd =\",\n \"a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a\",\n \"NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\",\n \"'\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u',\",\n \"tblName, colName, newVal, fromCondition=None): ''' Update one column with a new value. :param\",\n \":param colVals: list of column values destined for a MySQL table :type colVals:\",\n \"0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s,\",\n \"must retain the above copyright notice, this list of conditions and the following\",\n \"that the following conditions are met: # 1. Redistributions of source code must\",\n \"Stanford University # All rights reserved. # # Redistribution and use in source\",\n \"self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd,\",\n \"STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT\",\n \"column names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param\",\n \"types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items():\",\n \"of new table :type tableName: String :param schema: dictionary mapping column names to\",\n \"column values, insert the data into a specified table :param tblName: name of\",\n \"HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\",\n \"(str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order of\\\\ values\",\n \"into :type tblName: String :param colnameValueDict: mapping of column name to column value\",\n \"Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work:\",\n \"finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with\",\n \"to endorse or promote products derived from this software without specific prior written\",\n \"products derived from this software without specific prior written permission. # # THIS\",\n \"log in as :type user: string :param passwd: password to use for given\",\n \"hold the values. Order of\\\\ values must corresond to order of column names\",\n \":param fromCondition: optionally condition that selects which rows to update.\\\\ if None, the\",\n \"cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create\",\n \"mappingt column names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'}\",\n \"PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS\",\n \"of conditions and the following disclaimer. # 2. Redistributions in binary form must\",\n \"import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some\",\n \"self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>,\",\n \"Strategy: write the values to a temp file, then generate a LOAD INFILE\",\n \"a temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute that\",\n \"finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return a\",\n \"TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor()\",\n \"forms, with or without modification, are permitted provided that the following conditions are\",\n \"condition that selects which rows to update.\\\\ if None, the named column in\",\n \"[] for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el)\",\n \"class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The query()\",\n \"close() method ''' import re import subprocess import tempfile import pymysql #import MySQLdb\",\n \"passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) >\",\n \"''' Delete table safely. No errors :param tableName: name of table :type tableName:\",\n \"rows are updated to\\\\ the given value. Syntax must conform to what may\",\n \"in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self, tableName,\",\n \"string :param db: database to connect to within server :type db: string '''\",\n \"try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user,\",\n \"INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close()\",\n \"IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\",\n \"following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright\",\n \"Order of\\\\ values must corresond to order of column names in colNameTuple. :type\",\n \"tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit()\",\n \"% (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list\",\n \"MySQL for the given column :param fromCondition: optionally condition that selects which rows\",\n \"safely. No errors :param tableName: name of table :type tableName: String ''' cursor\",\n \":param valueTupleArray: array of n-tuples, which hold the values. Order of\\\\ values must\",\n \"schema): ''' Create new table, given its name, and schema. The schema is\",\n \"SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def\",\n \"[] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port,\",\n \"databases. Some niceties nonetheless. The query() method is an iterator. So:: for result\",\n \"dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name of table\",\n \"to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold\",\n \"name of table in which update is to occur :type tblName: String :param\",\n \"must corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) '''\",\n \"MySQL typing. Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList))\",\n \"else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query,\",\n \"passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param port: MySQL\",\n \"',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName,\",\n \"mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column\",\n \"' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS\",\n \"in this MySQL version...' even though MySQL is set up to allow the\",\n \"iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def\",\n \"table safely. No errors :param tableName: name of table :type tableName: String '''\",\n \"colName, newVal, fromCondition=None): ''' Update one column with a new value. :param tblName:\",\n \"of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s'\",\n \"Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in\",\n \"foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param\",\n \"THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.\",\n \"values, insert the data into a specified table :param tblName: name of table\",\n \":type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None: cmd\",\n \"% (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s = '%s' WHERE %s;\\\"\",\n \"= [] for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else:\",\n \"MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL\",\n \"the following conditions are met: # 1. Redistributions of source code must retain\",\n \"to use for given user :type passwd: string :param db: database to connect\",\n \"to column types :type schema: Dict<String,String> ''' colSpec = '' for colName, colVal\",\n \"in my.cnf). :param tblName: table into which to insert :type tblName: string :param\",\n \"tblName: name of table in which update is to occur :type tblName: String\",\n \"a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String '''\",\n \":param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For if\",\n \"db='mysql'): ''' :param host: MySQL host :type host: string :param port: MySQL host's\",\n \"+ '\\\\n') try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList\",\n \"str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF\",\n \"of source code must retain the above copyright notice, this list of conditions\",\n \"INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\",\n \"Poem') ---> '10, My Poem' :param colVals: list of column values destined for\",\n \"subsequent call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() #\",\n \"= re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE\",\n \"%s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally:\",\n \"must conform to what may be in\\\\ a MySQL FROM clause (don't include\",\n \"'%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES\",\n \"colSpec += str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE\",\n \"conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce\",\n \"subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal,\",\n \"None, we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return\",\n \"= user self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection\",\n \"OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\",\n \"allow the op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName:\",\n \"self.user = user self.pwd = <PASSWORD> self.db = db self.cursors = [] try:\",\n \"= '...............' if len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL\",\n \"name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE\",\n \"LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user,\",\n \"selects which rows to update.\\\\ if None, the named column in all rows\",\n \"column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple\",\n \"server :type db: string ''' # If all arguments are set to None,\",\n \"rows. No errors :param tableName: name of table :type tableName: String ''' cursor\",\n \"'CREATE TABLE IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor =\",\n \"WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given\",\n \"other materials provided with the distribution. # 3. Neither the name of the\",\n \"# # Redistribution and use in source and binary forms, with or without\",\n \"ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\",\n \"port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if\",\n \"''' Close all cursors that are currently still open. ''' for cursor in\",\n \"zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)'\",\n \"permitted provided that the following conditions are met: # 1. Redistributions of source\",\n \"= MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd =\",\n \"of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF\",\n \"len(passwd) > 0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s,\",\n \"POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications:\",\n \"%s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table\",\n \"# Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd\",\n \"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND\",\n \"''' import re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object):\",\n \"TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\",\n \"Some niceties nonetheless. The query() method is an iterator. So:: for result in\",\n \": 'TEXT'} :param tableName: name of new table :type tableName: String :param schema:\",\n \"query() method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'):\",\n \"# Copyright (c) 2014, Stanford University # All rights reserved. # # Redistribution\",\n \"def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type\",\n \"colVals: list of column values destined for a MySQL table :type colVals: <any>\",\n \"''' Update one column with a new value. :param tblName: name of table\",\n \"FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER\",\n \"2013: Added closing of connection to close() method ''' import re import subprocess\",\n \"WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\",\n \"for result in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1',\",\n \"to within server :type db: string ''' # If all arguments are set\",\n \"''' colSpec = '' for colName, colVal in schema.items(): colSpec += str(colName) +\",\n \"re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s\",\n \"' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s\",\n \"is to occur :type tblName: String :param colName: column whose value is to\",\n \"= '' for colName, colVal in schema.items(): colSpec += str(colName) + ' '\",\n \"dictionary mapping column names to column values, insert the data into a specified\",\n \"corresond to order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile\",\n \"values. Order of\\\\ values must corresond to order of column names in colNameTuple.\",\n \"''' Query iterator. Given a query, return one result for each subsequent call.\",\n \"prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS\",\n \"BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\",\n \"(%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple,\",\n \"modification, are permitted provided that the following conditions are met: # 1. Redistributions\",\n \"may be used to endorse or promote products derived from this software without\",\n \"OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\",\n \"i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of\",\n \"[(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try:\",\n \"OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author:\",\n \"tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a\",\n \"by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes is\",\n \"column with a new value. :param tblName: name of table in which update\",\n \"in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from the values inside\",\n \"Given a query, return one result for each subsequent call. :param queryStr: query\",\n \"IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24,\",\n \"(host, port, user, pwd, db)) def close(self): ''' Close all cursors that are\",\n \"my.cnf). :param tblName: table into which to insert :type tblName: string :param colNameTuple:\",\n \"host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string\",\n \"MySQL host :type host: string :param port: MySQL host's port :type port: int\",\n \"{'col1' : 'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type\",\n \"print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host:\",\n \"'col2' : 'TEXT'} :param tableName: name of new table :type tableName: String :param\",\n \"list of conditions and the following disclaimer. # 2. Redistributions in binary form\",\n \"for each subsequent call. :param queryStr: query :type queryStr: String ''' cursor =\",\n \"ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE\",\n \"OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY\",\n \"file, then generate a LOAD INFILE LOCAL MySQL command. Execute that command via\",\n \"MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless. The\",\n \"LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY\",\n \"TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd,\",\n \"n-tuples, which hold the values. Order of\\\\ values must corresond to order of\",\n \"if all(arg is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd\",\n \"%s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a\",\n \"def createTable(self, tableName, schema): ''' Create new table, given its name, and schema.\",\n \"the given column :type newVal: type acceptable to MySQL for the given column\",\n \"are currently still open. ''' for cursor in self.cursors: try: cursor.close() except: pass\",\n \"with a new value. :param tblName: name of table in which update is\",\n \"import re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): '''\",\n \"try: self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table, given\",\n \"return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return one\",\n \"''' Inserts large number of rows into given table. Strategy: write the values\",\n \"self.connection.cursor() try: if fromCondition is None: cmd = \\\"UPDATE %s SET %s =\",\n \"INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\",\n \"INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT\",\n \"pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close all\",\n \"self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self, tblName,\",\n \"'%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s = '%s' WHERE\",\n \"colNameTuple: tuple containing column names in proper order, i.e. \\\\ corresponding to valueTupleArray\",\n \"'\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd])\",\n \"Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30, 2013: Added\",\n \"Delete table safely. No errors :param tableName: name of table :type tableName: String\",\n \"String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName)\",\n \"# All rights reserved. # # Redistribution and use in source and binary\",\n \"cursors that are currently still open. ''' for cursor in self.cursors: try: cursor.close()\",\n \"try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No\",\n \"INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED\",\n \"the given value. Syntax must conform to what may be in\\\\ a MySQL\",\n \"FROM clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor =\",\n \"finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names\",\n \"disclaimer in the documentation and/or other materials provided with the distribution. # 3.\",\n \"given table. Strategy: write the values to a temp file, then generate a\",\n \"db: string ''' # If all arguments are set to None, we are\",\n \"schema is a dict mappingt column names to column types. Example: {'col1' :\",\n \"table :param tblName: name of table to insert into :type tblName: String :param\",\n \"None: cmd = \\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd\",\n \"= \\\"UPDATE %s SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit()\",\n \"tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self,\",\n \"%s = '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s =\",\n \"tuple containing column names in proper order, i.e. \\\\ corresponding to valueTupleArray orders.\",\n \"in\\\\ a MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String\",\n \"contributors may be used to endorse or promote products derived from this software\",\n \"THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created\",\n \"from the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s;\",\n \"CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\",\n \"port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db, local_infile=1) #except\",\n \"newVal: value acceptable to MySQL for the given column :type newVal: type acceptable\",\n \"this list of conditions and the following disclaimer in the documentation and/or other\",\n \"EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\",\n \"(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF\",\n \"= 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit()\",\n \"str(colVal) + ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) '\",\n \"all table rows. No errors :param tableName: name of table :type tableName: String\",\n \"of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues =\",\n \"else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s,\",\n \"Redistributions in binary form must reproduce the above copyright notice, this list of\",\n \"String :param colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any>\",\n \"LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\",\n \"work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list of column\",\n \"cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number\",\n \"colVals: <any> ''' resList = [] for el in colVals: if isinstance(el, basestring):\",\n \"materials provided with the distribution. # 3. Neither the name of the copyright\",\n \"String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally:\",\n \"order, i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array\",\n \"DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\",\n \"> 0 else '<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s,\",\n \"EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit()\",\n \"try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self,\",\n \"following conditions are met: # 1. Redistributions of source code must retain the\",\n \"self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames),\",\n \"HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT\",\n \"via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not\",\n \"tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE\",\n \"for the given column :param fromCondition: optionally condition that selects which rows to\",\n \"IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\",\n \"to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor =\",\n \"order of column names in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv')\",\n \"of connection to close() method ''' import re import subprocess import tempfile import\",\n \":param colName: column whose value is to be changed :type colName: String :param\",\n \"%\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql', '-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close()\",\n \"LOCAL is not supported in this MySQL version...' even though MySQL is set\",\n \"provided with the distribution. # 3. Neither the name of the copyright holder\",\n \"Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem'\",\n \"used to endorse or promote products derived from this software without specific prior\",\n \"for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from the\",\n \"binary form must reproduce the above copyright notice, this list of conditions and\",\n \"'My Poem') ---> '10, My Poem' :param colVals: list of column values destined\",\n \"\\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s\",\n \"password to use for given user :type passwd: string :param db: database to\",\n \"' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def\",\n \"DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\",\n \"truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName: name of\",\n \"IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY\",\n \"EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS\",\n \"even though MySQL is set up to allow the op (load-infile=1 for both\",\n \"LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY\",\n \"TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\\",\n \"mapping column names to column values, insert the data into a specified table\",\n \"are met: # 1. Redistributions of source code must retain the above copyright\",\n \"finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows\",\n \"try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple))\",\n \"name of new table :type tableName: String :param schema: dictionary mapping column names\",\n \"IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN\",\n \"Copyright (c) 2014, Stanford University # All rights reserved. # # Redistribution and\",\n \"into which to insert :type tblName: string :param colNameTuple: tuple containing column names\",\n \"queryStr): ''' Query iterator. Given a query, return one result for each subsequent\",\n \"the documentation and/or other materials provided with the distribution. # 3. Neither the\",\n \"the above copyright notice, this list of conditions and the following disclaimer. #\",\n \"typing. Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't\",\n \"host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self):\",\n \"= zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES\",\n \"names to column types :type schema: Dict<String,String> ''' colSpec = '' for colName,\",\n \"retain the above copyright notice, this list of conditions and the following disclaimer.\",\n \"column :type newVal: type acceptable to MySQL for the given column :param fromCondition:\",\n \"ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\",\n \"keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is None:\",\n \"table in which update is to occur :type tblName: String :param colName: column\",\n \"bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table.\",\n \"+ ',' cmd = 'CREATE TABLE IF NOT EXISTS %s (%s) ' %\",\n \"command. Execute that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD\",\n \"''' cursor = self.connection.cursor() # For if caller never exhausts the results by\",\n \"BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\",\n \"---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') --->\",\n \"insert :type tblName: string :param colNameTuple: tuple containing column names in proper order,\",\n \"OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT\",\n \"''' Shallow interface to MySQL databases. Some niceties nonetheless. The query() method is\",\n \"%s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self,\",\n \"% el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given\",\n \":type user: string :param passwd: password to use for given user :type passwd:\",\n \"University # All rights reserved. # # Redistribution and use in source and\",\n \"user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param port:\",\n \"self.connection.close() except: pass def createTable(self, tableName, schema): ''' Create new table, given its\",\n \"of rows into given table. Strategy: write the values to a temp file,\",\n \"server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db))\",\n \"mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='',\",\n \"Delete all table rows. No errors :param tableName: name of table :type tableName:\",\n \"insert into :type tblName: String :param colnameValueDict: mapping of column name to column\",\n \"both mysql and mysqld in my.cnf). :param tblName: table into which to insert\",\n \"column whose value is to be changed :type colName: String :param newVal: value\",\n \"in all rows are updated to\\\\ the given value. Syntax must conform to\",\n \"LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF\",\n \"for a MySQL table :type colVals: <any> ''' resList = [] for el\",\n \"update.\\\\ if None, the named column in all rows are updated to\\\\ the\",\n \"to MySQL databases. Some niceties nonetheless. The query() method is an iterator. So::\",\n \"tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' %\",\n \"database to connect to within server :type db: string ''' # If all\",\n \"# 1. Redistributions of source code must retain the above copyright notice, this\",\n \"import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL\",\n \"fromCondition=None): ''' Update one column with a new value. :param tblName: name of\",\n \"#import MySQLdb class MySQLDB(object): ''' Shallow interface to MySQL databases. Some niceties nonetheless.\",\n \"cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items,\",\n \"disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice,\",\n \"password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' %\",\n \"column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items())\",\n \"= tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes\",\n \"%s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n'\",\n \"in binary form must reproduce the above copyright notice, this list of conditions\",\n \"of conditions and the following disclaimer in the documentation and/or other materials provided\",\n \"Inserts large number of rows into given table. Strategy: write the values to\",\n \":type newVal: type acceptable to MySQL for the given column :param fromCondition: optionally\",\n \"SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\",\n \":type colnameValueDict: Dict<String,Any> ''' colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd\",\n \"value. Syntax must conform to what may be in\\\\ a MySQL FROM clause\",\n \"OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\",\n \"tableName): ''' Delete table safely. No errors :param tableName: name of table :type\",\n \"update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a new value.\",\n \"''' cursor = self.connection.cursor() try: cursor.execute('DROP TABLE IF EXISTS %s' % tableName) self.connection.commit()\",\n \"\\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My\",\n \"def update(self, tblName, colName, newVal, fromCondition=None): ''' Update one column with a new\",\n \"include the 'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if\",\n \"(tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close() def dropTable(self, tableName):\",\n \"Poem' :param colVals: list of column values destined for a MySQL table :type\",\n \"op (load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table into\",\n \":type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order\",\n \"','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals: list\",\n \"nor the names of its contributors may be used to endorse or promote\",\n \"code must retain the above copyright notice, this list of conditions and the\",\n \"SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec\",\n \"arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD> self.db = db\",\n \"resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el)\",\n \"and binary forms, with or without modification, are permitted provided that the following\",\n \"is set up to allow the op (load-infile=1 for both mysql and mysqld\",\n \"INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"'\",\n \"not supported in this MySQL version...' even though MySQL is set up to\",\n \"up to allow the op (load-infile=1 for both mysql and mysqld in my.cnf).\",\n \"Given a list of items, return a string that preserves MySQL typing. Example:\",\n \"Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param\",\n \"call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor() # For\",\n \"self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of items, return\",\n \"source code must retain the above copyright notice, this list of conditions and\",\n \"schema.items(): colSpec += str(colName) + ' ' + str(colVal) + ',' cmd =\",\n \"ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\",\n \"el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a\",\n \"THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke\",\n \"OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED\",\n \"open. ''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except:\",\n \"in proper order, i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param\",\n \"generate a LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using\",\n \"closing of connection to close() method ''' import re import subprocess import tempfile\",\n \"(%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try: cursor.execute(cmd) self.connection.commit() finally: cursor.close()\",\n \"without modification, are permitted provided that the following conditions are met: # 1.\",\n \"LOAD INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute()\",\n \"basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query\",\n \"MySQL host's port :type port: int :param user: user to log in as\",\n \"OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF\",\n \"table into which to insert :type tblName: string :param colNameTuple: tuple containing column\",\n \"MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd,\",\n \"FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\"\",\n \"command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is\",\n \"def close(self): ''' Close all cursors that are currently still open. ''' for\",\n \"given user :type passwd: string :param db: database to connect to within server\",\n \"of table to insert into :type tblName: String :param colnameValueDict: mapping of column\",\n \"fails with error 'LOAD DATA LOCAL is not supported in this MySQL version...'\",\n \"table :type colVals: <any> ''' resList = [] for el in colVals: if\",\n \"schema: Dict<String,String> ''' colSpec = '' for colName, colVal in schema.items(): colSpec +=\",\n \"IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY\",\n \"values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA\",\n \"that are currently still open. ''' for cursor in self.cursors: try: cursor.close() except:\",\n \":param passwd: password to use for given user :type passwd: string :param db:\",\n \"tblName: String :param colName: column whose value is to be changed :type colName:\",\n \"endorse or promote products derived from this software without specific prior written permission.\",\n \"given value. Syntax must conform to what may be in\\\\ a MySQL FROM\",\n \"with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def\",\n \"of its contributors may be used to endorse or promote products derived from\",\n \"2013 @author: paepcke Modifications: - Dec 30, 2013: Added closing of connection to\",\n \"DAMAGE. ''' Created on Sep 24, 2013 @author: paepcke Modifications: - Dec 30,\",\n \"db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection\",\n \"self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors\",\n \"reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s' % (host, port, user,\",\n \"mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO\",\n \"given column :param fromCondition: optionally condition that selects which rows to update.\\\\ if\",\n \"- Dec 30, 2013: Added closing of connection to close() method ''' import\",\n \"list of column values destined for a MySQL table :type colVals: <any> '''\",\n \"documentation and/or other materials provided with the distribution. # 3. Neither the name\",\n \"<any> ''' resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"'\",\n \"db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close all cursors\",\n \"pwd, db)) def close(self): ''' Close all cursors that are currently still open.\",\n \"newVal, fromCondition=None): ''' Update one column with a new value. :param tblName: name\",\n \"version...' even though MySQL is set up to allow the op (load-infile=1 for\",\n \"OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. '''\",\n \"Dec 30, 2013: Added closing of connection to close() method ''' import re\",\n \"values to a temp file, then generate a LOAD INFILE LOCAL MySQL command.\",\n \"a dict mappingt column names to column types. Example: {'col1' : 'INT', 'col2'\",\n \"CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\",\n \"tableName: name of new table :type tableName: String :param schema: dictionary mapping column\",\n \"FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\",\n \"values destined for a MySQL table :type colVals: <any> ''' resList = []\",\n \"return one result for each subsequent call. :param queryStr: query :type queryStr: String\",\n \"colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which hold the values. Order of\\\\\",\n \"large number of rows into given table. Strategy: write the values to a\",\n \"into given table. Strategy: write the values to a temp file, then generate\",\n \"BY '\\\\\\\"' LINES TERMINATED BY '\\\\\\\\n' %s\\\" %\\\\ (self.db, tmpCSVFile.name, tblName, mySQLColNameList) subprocess.call(['mysql',\",\n \"return a string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10,\",\n \"BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\",\n \"\\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\",\n \"No errors :param tableName: name of table :type tableName: String ''' cursor =\",\n \"an iterator. So:: for result in mySqlObj.query('SELECT * FROM foo'): print result '''\",\n \"AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\",\n \"self.connection.commit() finally: cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column\",\n \"VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName,\",\n \"temp file, then generate a LOAD INFILE LOCAL MySQL command. Execute that command\",\n \"cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' %\",\n \"valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from the values\",\n \"cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def\",\n \"dict mappingt column names to column types. Example: {'col1' : 'INT', 'col2' :\",\n \"= self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' % tableName) self.connection.commit() finally: cursor.close() def insert(self,\",\n \"that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \\\"My Poem\\\"' Note\",\n \"host: MySQL host :type host: string :param port: MySQL host's port :type port:\",\n \"column names in proper order, i.e. \\\\ corresponding to valueTupleArray orders. :type colNameTuple:\",\n \"for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return\",\n \"destined for a MySQL table :type colVals: <any> ''' resList = [] for\",\n \"try: if fromCondition is None: cmd = \\\"UPDATE %s SET %s = '%s';\\\"\",\n \"in colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray:\",\n \"form must reproduce the above copyright notice, this list of conditions and the\",\n \"new table, given its name, and schema. The schema is a dict mappingt\",\n \"or without modification, are permitted provided that the following conditions are met: #\",\n \"= self.connection.cursor() # For if caller never exhausts the results by repeated calls:\",\n \"paepcke Modifications: - Dec 30, 2013: Added closing of connection to close() method\",\n \"cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName: name\",\n \"tblName, colnameValueDict): ''' Given a dictionary mapping column names to column values, insert\",\n \"of n-tuples, which hold the values. Order of\\\\ values must corresond to order\",\n \"above copyright notice, this list of conditions and the following disclaimer in the\",\n \"which rows to update.\\\\ if None, the named column in all rows are\",\n \"1. Redistributions of source code must retain the above copyright notice, this list\",\n \"into a specified table :param tblName: name of table to insert into :type\",\n \"String ''' cursor = self.connection.cursor() # For if caller never exhausts the results\",\n \"and use in source and binary forms, with or without modification, are permitted\",\n \"result for each subsequent call. :param queryStr: query :type queryStr: String ''' cursor\",\n \"use in source and binary forms, with or without modification, are permitted provided\",\n \"notice, this list of conditions and the following disclaimer. # 2. Redistributions in\",\n \"number of rows into given table. Strategy: write the values to a temp\",\n \":param tableName: name of new table :type tableName: String :param schema: dictionary mapping\",\n \"table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('TRUNCATE TABLE %s' %\",\n \"copyright notice, this list of conditions and the following disclaimer in the documentation\",\n \"MySQL is set up to allow the op (load-infile=1 for both mysql and\",\n \"# 3. Neither the name of the copyright holder nor the names of\",\n \"DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED\",\n \"exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone()\",\n \"copyright notice, this list of conditions and the following disclaimer. # 2. Redistributions\",\n \"% (host, port, user, pwd, db)) def close(self): ''' Close all cursors that\",\n \":param tblName: name of table to insert into :type tblName: String :param colnameValueDict:\",\n \"colNames, colValues = zip(*colnameValueDict.items()) cursor = self.connection.cursor() try: cmd = 'INSERT INTO %s\",\n \"port: int :param user: user to log in as :type user: string :param\",\n \"a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported in this\",\n \"# If all arguments are set to None, we are unittesting: if all(arg\",\n \"newVal: type acceptable to MySQL for the given column :param fromCondition: optionally condition\",\n \"IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS\",\n \"THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\",\n \"cursor.close() def insert(self, tblName, colnameValueDict): ''' Given a dictionary mapping column names to\",\n \"= self.connection.cursor() try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName),\",\n \"the names of its contributors may be used to endorse or promote products\",\n \"except: pass def createTable(self, tableName, schema): ''' Create new table, given its name,\",\n \"MySQL for the given column :type newVal: type acceptable to MySQL for the\",\n \"# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\"\",\n \"INFILE LOCAL MySQL command. Execute that command via subprocess.call(). Using a cursor.execute() fails\",\n \"a new value. :param tblName: name of table in which update is to\",\n \"in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def\",\n \"self.pwd = <PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host,\",\n \"tableName: name of table :type tableName: String ''' cursor = self.connection.cursor() try: cursor.execute('DROP\",\n \"IF EXISTS %s' % tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete\",\n \"CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR\",\n \"NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,\",\n \"'FROM' keyword) :type fromCondition: String ''' cursor = self.connection.cursor() try: if fromCondition is\",\n \"MySQL FROM clause (don't include the 'FROM' keyword) :type fromCondition: String ''' cursor\",\n \"Given a dictionary mapping column names to column values, insert the data into\",\n \"which to insert :type tblName: string :param colNameTuple: tuple containing column names in\",\n \"a MySQL table :type colVals: <any> ''' resList = [] for el in\",\n \"Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem')\",\n \"Syntax must conform to what may be in\\\\ a MySQL FROM clause (don't\",\n \"\\\"USE %s; LOAD DATA LOCAL INFILE '%s' INTO TABLE %s FIELDS TERMINATED BY\",\n \"DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS\",\n \"ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\",\n \"try: cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues))\",\n \"SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s\",\n \"nonetheless. The query() method is an iterator. So:: for result in mySqlObj.query('SELECT *\",\n \"cursor.close() def truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName:\",\n \"we are unittesting: if all(arg is None for arg in (host,port,user,passwd,db)): return self.user\",\n \"are set to None, we are unittesting: if all(arg is None for arg\",\n \"A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\",\n \"user=user, passwd=passwd, db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd)\",\n \"value is to be changed :type colName: String :param newVal: value acceptable to\",\n \"= '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals):\",\n \"caller never exhausts the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes\",\n \"all cursors that are currently still open. ''' for cursor in self.cursors: try:\",\n \"'<no password>' raise ValueError('Cannot reach MySQL server with host:%s, port:%s, user:%s, pwd:%s, db:%s'\",\n \"WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO\",\n \":param colNameTuple: tuple containing column names in proper order, i.e. \\\\ corresponding to\",\n \"PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\",\n \"<PASSWORD> self.db = db self.cursors = [] try: self.connection = pymysql.connect(host=host, port=port, user=user,\",\n \"ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' Created on Sep 24, 2013\",\n \"string :param colNameTuple: tuple containing column names in proper order, i.e. \\\\ corresponding\",\n \"to column values, insert the data into a specified table :param tblName: name\",\n \"acceptable to MySQL for the given column :param fromCondition: optionally condition that selects\",\n \"local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0 else\",\n \"tblName: table into which to insert :type tblName: string :param colNameTuple: tuple containing\",\n \"source and binary forms, with or without modification, are permitted provided that the\",\n \"cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close() except: pass def createTable(self,\",\n \"SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\",\n \"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\",\n \"OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\",\n \"each subsequent call. :param queryStr: query :type queryStr: String ''' cursor = self.connection.cursor()\",\n \"reserved. # # Redistribution and use in source and binary forms, with or\",\n \"__init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host:\",\n \"a list of items, return a string that preserves MySQL typing. Example: (10,\",\n \"of items, return a string that preserves MySQL typing. Example: (10, 'My Poem')\",\n \"isinstance(el, basestring): resList.append('\\\"%s\\\"' % el) else: resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): '''\",\n \"def query(self, queryStr): ''' Query iterator. Given a query, return one result for\",\n \"mapping of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames, colValues\",\n \"= pymysql.connect(host=host, port=port, user=user, passwd=<PASSWORD>, db=db) #self.connection = MySQLdb.connect(host=host, port=port, user=user, passwd=passwd, db=db,\",\n \"the distribution. # 3. Neither the name of the copyright holder nor the\",\n \"''' # If all arguments are set to None, we are unittesting: if\",\n \"def truncateTable(self, tableName): ''' Delete all table rows. No errors :param tableName: name\",\n \"'-u', self.user, '-p%s'%self.pwd, '-e', mySQLCmd]) finally: tmpCSVFile.close() def update(self, tblName, colName, newVal, fromCondition=None):\",\n \"WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\",\n \"given its name, and schema. The schema is a dict mappingt column names\",\n \"to close() method ''' import re import subprocess import tempfile import pymysql #import\",\n \"subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL is not supported\",\n \":type tblName: String :param colName: column whose value is to be changed :type\",\n \"the given column :param fromCondition: optionally condition that selects which rows to update.\\\\\",\n \"\\\"UPDATE %s SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally:\",\n \"one column with a new value. :param tblName: name of table in which\",\n \"that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10, My Poem' :param colVals:\",\n \"the copyright holder nor the names of its contributors may be used to\",\n \"MySQL databases. Some niceties nonetheless. The query() method is an iterator. So:: for\",\n \"+= str(colName) + ' ' + str(colVal) + ',' cmd = 'CREATE TABLE\",\n \"of table in which update is to occur :type tblName: String :param colName:\",\n \"acceptable to MySQL for the given column :type newVal: type acceptable to MySQL\",\n \"''' resList = [] for el in colVals: if isinstance(el, basestring): resList.append('\\\"%s\\\"' %\",\n \"is None for arg in (host,port,user,passwd,db)): return self.user = user self.pwd = <PASSWORD>\",\n \"that selects which rows to update.\\\\ if None, the named column in all\",\n \"holder nor the names of its contributors may be used to endorse or\",\n \"None, the named column in all rows are updated to\\\\ the given value.\",\n \"NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\",\n \"'10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My Poem') ---> '10,\",\n \"tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: # Remove quotes from\",\n \":param colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any> '''\",\n \"is to be changed :type colName: String :param newVal: value acceptable to MySQL\",\n \"above copyright notice, this list of conditions and the following disclaimer. # 2.\",\n \"re import subprocess import tempfile import pymysql #import MySQLdb class MySQLDB(object): ''' Shallow\",\n \"% tableName) self.connection.commit() finally: cursor.close() def truncateTable(self, tableName): ''' Delete all table rows.\",\n \"for both mysql and mysqld in my.cnf). :param tblName: table into which to\",\n \"column in all rows are updated to\\\\ the given value. Syntax must conform\",\n \":type tblName: string :param colNameTuple: tuple containing column names in proper order, i.e.\",\n \"updated to\\\\ the given value. Syntax must conform to what may be in\\\\\",\n \"host: string :param port: MySQL host's port :type port: int :param user: user\",\n \"The query() method is an iterator. So:: for result in mySqlObj.query('SELECT * FROM\",\n \"names to column types. Example: {'col1' : 'INT', 'col2' : 'TEXT'} :param tableName:\",\n \"the values to a temp file, then generate a LOAD INFILE LOCAL MySQL\",\n \"is not supported in this MySQL version...' even though MySQL is set up\",\n \"(tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def ensureSQLTyping(self, colVals): ''' Given a list of\",\n \"query(self, queryStr): ''' Query iterator. Given a query, return one result for each\",\n \"schema: dictionary mapping column names to column types :type schema: Dict<String,String> ''' colSpec\",\n \":type host: string :param port: MySQL host's port :type port: int :param user:\",\n \"is None: cmd = \\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal) else:\",\n \"distribution. # 3. Neither the name of the copyright holder nor the names\",\n \"query :type queryStr: String ''' cursor = self.connection.cursor() # For if caller never\",\n \"what may be in\\\\ a MySQL FROM clause (don't include the 'FROM' keyword)\",\n \"USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\",\n \"result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL\",\n \"Update one column with a new value. :param tblName: name of table in\",\n \"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\",\n \"resList.append(el) return ','.join(map(str,resList)) def query(self, queryStr): ''' Query iterator. Given a query, return\",\n \"IF NOT EXISTS %s (%s) ' % (tableName, colSpec[:-1]) cursor = self.connection.cursor() try:\",\n \"user:%s, pwd:%s, db:%s' % (host, port, user, pwd, db)) def close(self): ''' Close\",\n \"iterator. Given a query, return one result for each subsequent call. :param queryStr:\",\n \"def ensureSQLTyping(self, colVals): ''' Given a list of items, return a string that\",\n \"(str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self, tblName, colNameTuple, valueTupleArray): '''\",\n \"cmd = \\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd =\",\n \"queryStr: String ''' cursor = self.connection.cursor() # For if caller never exhausts the\",\n \"close(self): ''' Close all cursors that are currently still open. ''' for cursor\",\n \"colNameTuple. :type valueTupleArray: [(<anyMySQLCompatibleTypes>[<anyMySQLCompatibleTypes,...]]) ''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple)\",\n \"to log in as :type user: string :param passwd: password to use for\",\n \"from this software without specific prior written permission. # # THIS SOFTWARE IS\",\n \"''' :param host: MySQL host :type host: string :param port: MySQL host's port\",\n \"interface to MySQL databases. Some niceties nonetheless. The query() method is an iterator.\",\n \"tableName: String :param schema: dictionary mapping column names to column types :type schema:\",\n \"colNameTuple, valueTupleArray): ''' Inserts large number of rows into given table. Strategy: write\",\n \"name of the copyright holder nor the names of its contributors may be\",\n \"%s SET %s = '%s' WHERE %s;\\\" % (tblName,colName,newVal,fromCondition) cursor.execute(cmd) self.connection.commit() finally: cursor.close()\",\n \"WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\",\n \"(%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally: cursor.close() def bulkInsert(self,\",\n \"# # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS\",\n \"niceties nonetheless. The query() method is an iterator. So:: for result in mySqlObj.query('SELECT\",\n \"column :param fromCondition: optionally condition that selects which rows to update.\\\\ if None,\",\n \"copyright holder nor the names of its contributors may be used to endorse\",\n \"arguments are set to None, we are unittesting: if all(arg is None for\",\n \"the results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if\",\n \"''' Delete all table rows. No errors :param tableName: name of table :type\",\n \"String :param colName: column whose value is to be changed :type colName: String\",\n \"---> '10, My Poem' :param colVals: list of column values destined for a\",\n \"provided that the following conditions are met: # 1. Redistributions of source code\",\n \"user to log in as :type user: string :param passwd: password to use\",\n \": 'INT', 'col2' : 'TEXT'} :param tableName: name of new table :type tableName:\",\n \"port=3306, user='root', passwd='', db='mysql'): ''' :param host: MySQL host :type host: string :param\",\n \"self.connection.commit() finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param\",\n \"'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd) self.connection.commit() finally:\",\n \"'\\\\n') try: # Remove quotes from the values inside the colNameTuple's: mySQLColNameList =\",\n \"'My Poem') ---> '10, \\\"My Poem\\\"' Note that ','.join(map(str,myList)) won't work: (10, 'My\",\n \"conditions are met: # 1. Redistributions of source code must retain the above\",\n \"pass def createTable(self, tableName, schema): ''' Create new table, given its name, and\",\n \"corresponding to valueTupleArray orders. :type colNameTuple: (str[,str[...]]) :param valueTupleArray: array of n-tuples, which\",\n \"= '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE %s SET %s = '%s'\",\n \"derived from this software without specific prior written permission. # # THIS SOFTWARE\",\n \"(load-infile=1 for both mysql and mysqld in my.cnf). :param tblName: table into which\",\n \"of\\\\ values must corresond to order of column names in colNameTuple. :type valueTupleArray:\",\n \"as :type user: string :param passwd: password to use for given user :type\",\n \"still open. ''' for cursor in self.cursors: try: cursor.close() except: pass try: self.connection.close()\",\n \"the data into a specified table :param tblName: name of table to insert\",\n \"user: user to log in as :type user: string :param passwd: password to\",\n \"results by repeated calls: self.cursors.append(cursor) cursor.execute(queryStr) while True: nextRes = cursor.fetchone() if nextRes\",\n \"OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\",\n \"''' Given a dictionary mapping column names to column values, insert the data\",\n \"valueTupleArray): ''' Inserts large number of rows into given table. Strategy: write the\",\n \"db=db, local_infile=1) #except MySQLdb.OperationalError: except pymysql.OperationalError: pwd = '...............' if len(passwd) > 0\",\n \"cmd = 'INSERT INTO %s (%s) VALUES (%s)' % (str(tblName), ','.join(colNames), self.ensureSQLTyping(colValues)) cursor.execute(cmd)\",\n \"value. :param tblName: name of table in which update is to occur :type\",\n \"'' for colName, colVal in schema.items(): colSpec += str(colName) + ' ' +\",\n \"if None, the named column in all rows are updated to\\\\ the given\",\n \"''' tmpCSVFile = tempfile.NamedTemporaryFile(dir='/tmp',prefix='userCountryTmp',suffix='.csv') for valueTuple in valueTupleArray: tmpCSVFile.write(','.join(valueTuple) + '\\\\n') try: #\",\n \"new value. :param tblName: name of table in which update is to occur\",\n \"AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\",\n \"2014, Stanford University # All rights reserved. # # Redistribution and use in\",\n \"the name of the copyright holder nor the names of its contributors may\",\n \"finally: cursor.close() def dropTable(self, tableName): ''' Delete table safely. No errors :param tableName:\",\n \"tblName: string :param colNameTuple: tuple containing column names in proper order, i.e. \\\\\",\n \"with error 'LOAD DATA LOCAL is not supported in this MySQL version...' even\",\n \"in mySqlObj.query('SELECT * FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root',\",\n \"+ ' ' + str(colVal) + ',' cmd = 'CREATE TABLE IF NOT\",\n \"written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\",\n \"* FROM foo'): print result ''' def __init__(self, host='127.0.0.1', port=3306, user='root', passwd='', db='mysql'):\",\n \"names of its contributors may be used to endorse or promote products derived\",\n \"TABLE %s FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '\\\\\\\"' LINES TERMINATED BY\",\n \"quotes from the values inside the colNameTuple's: mySQLColNameList = re.sub(\\\"'\\\",\\\"\\\",str(colNameTuple)) mySQLCmd = \\\"USE\",\n \"def bulkInsert(self, tblName, colNameTuple, valueTupleArray): ''' Inserts large number of rows into given\",\n \"colnameValueDict: mapping of column name to column value :type colnameValueDict: Dict<String,Any> ''' colNames,\",\n \"= \\\"UPDATE %s SET %s = '%s';\\\" % (tblName,colName,newVal) else: cmd = \\\"UPDATE\",\n \"specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT\",\n \"a string that preserves MySQL typing. Example: (10, 'My Poem') ---> '10, \\\"My\",\n \"String :param schema: dictionary mapping column names to column types :type schema: Dict<String,String>\",\n \"in the documentation and/or other materials provided with the distribution. # 3. Neither\",\n \"that command via subprocess.call(). Using a cursor.execute() fails with error 'LOAD DATA LOCAL\"\n]","isConversational":false}}},{"rowIdx":3472,"cells":{"ngram":{"kind":"list like","value":["FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private","lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\",","xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\",","print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex())","print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex()) print(\"Key ID:\", xkey.keyid().hex())","xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\",","print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex())","xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex()) print(\"Key ID:\",","XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain","xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex()) print(\"Key ID:\", xkey.keyid().hex()) print(\"XKey:\",","from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent","lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version)","import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\",","print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\",","xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\",","xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if","<reponame>treys/crypto-key-derivation #!./venv/bin/python from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin())","print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key)","number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public","XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth)","print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex())","xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\",","import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex())","= XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick())","if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex()) print(\"Key ID:\", xkey.keyid().hex()) print(\"XKey:\", xkey.to_xkey().decode('ascii'))","print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes():","#!./venv/bin/python from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey)","from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\",","code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private bytes:\", xkey.key.get_private_bytes().hex()) print(\"Public bytes:\", xkey.key.get_public_bytes().hex()) print(\"Key","one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Version:\", xkey.version) print(\"Depth:\", xkey.depth) print(\"Parent FP:\", xkey.parent_fp.hex()) print(\"Child"],"string":"[\n \"FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private\",\n \"lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\",\",\n \"xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\",\",\n \"print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex())\",\n \"print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex()) print(\\\"Key ID:\\\", xkey.keyid().hex())\",\n \"xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\",\",\n \"print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex())\",\n \"xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex()) print(\\\"Key ID:\\\",\",\n \"XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain\",\n \"xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex()) print(\\\"Key ID:\\\", xkey.keyid().hex()) print(\\\"XKey:\\\",\",\n \"from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent\",\n \"lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version)\",\n \"import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\",\",\n \"print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\",\",\n \"xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\",\",\n \"xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if\",\n \"<reponame>treys/crypto-key-derivation #!./venv/bin/python from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin())\",\n \"print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key)\",\n \"number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public\",\n \"XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth)\",\n \"print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex())\",\n \"xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\",\",\n \"import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex())\",\n \"= XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick())\",\n \"if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex()) print(\\\"Key ID:\\\", xkey.keyid().hex()) print(\\\"XKey:\\\", xkey.to_xkey().decode('ascii'))\",\n \"print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child number:\\\", xkey.child_number_with_tick()) print(\\\"Chain code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes():\",\n \"#!./venv/bin/python from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey)\",\n \"from lib.mbp32 import XKey from lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\",\",\n \"code:\\\", xkey.chain_code.hex()) print(\\\"Key:\\\", xkey.key) if xkey.key.get_private_bytes(): print(\\\"Private bytes:\\\", xkey.key.get_private_bytes().hex()) print(\\\"Public bytes:\\\", xkey.key.get_public_bytes().hex()) print(\\\"Key\",\n \"one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\\\"Version:\\\", xkey.version) print(\\\"Depth:\\\", xkey.depth) print(\\\"Parent FP:\\\", xkey.parent_fp.hex()) print(\\\"Child\"\n]","isConversational":false}}},{"rowIdx":3473,"cells":{"ngram":{"kind":"list like","value":["of tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image","tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x,","to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim =","spherical_distortion.util import * sample_order = 9 # Input resolution to examine def ang_fov(s):","print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel","image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x)","base level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y)","print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y)","corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y =","sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s)","FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =',","def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s)","Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners of each","At base level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =',","Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension","= 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for","tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s) #","print(' At base level', b) print(' FOV (x) =', fov_x) print(' FOV (y)","fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x)","range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners =","level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print('","tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print('","in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners","* sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:',","# Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in","=', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print('","(x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim)","import * sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical","dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b,","s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of","Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level',","of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b)","= tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s)","= tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners)","fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix","= compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x) print('","ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) #","tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV","# Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners of","s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners","Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s):","# Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base","compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x) print(' FOV","=', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =', fov_y/dim) ang_fov(sample_order)","each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print('","image corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y","from spherical_distortion.util import * sample_order = 9 # Input resolution to examine def","b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image","examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b,","print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x)","s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At","9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b","b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix","for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent","(y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =', fov_y/dim)","resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim","fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =',","dimension of tangent image corners = tangent_image_corners(b, s) # Corners of each tangent","FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =',"],"string":"[\n \"of tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image\",\n \"tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x,\",\n \"to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim =\",\n \"spherical_distortion.util import * sample_order = 9 # Input resolution to examine def ang_fov(s):\",\n \"print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel\",\n \"image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x)\",\n \"base level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y)\",\n \"print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y)\",\n \"corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y =\",\n \"sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s)\",\n \"FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =',\",\n \"def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s)\",\n \"Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners of each\",\n \"At base level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =',\",\n \"Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension\",\n \"= 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for\",\n \"tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s) #\",\n \"print(' At base level', b) print(' FOV (x) =', fov_x) print(' FOV (y)\",\n \"fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x)\",\n \"range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners =\",\n \"level', b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print('\",\n \"tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print('\",\n \"in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners\",\n \"* sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:',\",\n \"# Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in\",\n \"=', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print('\",\n \"(x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim)\",\n \"import * sample_order = 9 # Input resolution to examine def ang_fov(s): print('Spherical\",\n \"dim = tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b,\",\n \"s) for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of\",\n \"Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level',\",\n \"of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b)\",\n \"= tangent_image_dim(b, s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s)\",\n \"= tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners)\",\n \"fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix\",\n \"= compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x) print('\",\n \"ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b, s) #\",\n \"tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV\",\n \"# Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners of\",\n \"s) # Pixel dimension of tangent image corners = tangent_image_corners(b, s) # Corners\",\n \"Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s):\",\n \"# Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base\",\n \"compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =', fov_x) print(' FOV\",\n \"=', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =', fov_y/dim) ang_fov(sample_order)\",\n \"each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print('\",\n \"image corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x, fov_y\",\n \"from spherical_distortion.util import * sample_order = 9 # Input resolution to examine def\",\n \"b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent image\",\n \"examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim = tangent_image_dim(b,\",\n \"print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix (x)\",\n \"s) # Corners of each tangent image fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At\",\n \"9 # Input resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b\",\n \"b) print(' FOV (x) =', fov_x) print(' FOV (y) =', fov_y) print(' deg/pix\",\n \"for b in range(s): dim = tangent_image_dim(b, s) # Pixel dimension of tangent\",\n \"(y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =', fov_y/dim)\",\n \"resolution to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim\",\n \"fov_x, fov_y = compute_tangent_image_angular_resolution(corners) print(' At base level', b) print(' FOV (x) =',\",\n \"dimension of tangent image corners = tangent_image_corners(b, s) # Corners of each tangent\",\n \"FOV (y) =', fov_y) print(' deg/pix (x) =', fov_x/dim) print(' deg/pix (y) =',\"\n]","isConversational":false}}},{"rowIdx":3474,"cells":{"ngram":{"kind":"list like","value":["raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update","attr)) for attr in ['start', 'stop', 'step']]) return node except Exception as ex:","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other):","= self.args return var def set_name(self, name): \"\"\" Set the name for a","values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data)","the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in",": dict or None Context in which to evaluate the nodes. callback :","def set_name(self, name): \"\"\" Set the name of the node and update the","__iter__(self): num = len(self) for i in range(num): yield self[i] def __eq__(self, other):","self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key =","= nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ =","== \"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name):","node. If graph is `None`, this is the top-level graph. \"\"\" return self._graph","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return","> len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var,","in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if key","If an node with `value` already exists in the associated graph. KeyError If","'stop', 'step']]) return node except Exception as ex: # pragma: no cover messages","other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","performed on a node. Parameters ---------- target : cal The multi-dimensional variable used","'%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for","id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_","1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context,","return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of this node","integers or parameter node names. graph : Node or None Parent graph of","[] self._predeecessors = [] self._succesors = [] self.args = args if \"name\" in","\"\"\" tuple : Positional arguments which are used for executing this node. \"\"\"","\"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other,","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return","list(context) # Add the keyword arguments for node in nodes: # pylint:disable=W0621 value","information keyed by variable name. Returns ------- values : Node or tuple[object] Output","values of a variable corresponding to input index values. Parameters ---------- var :","{self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name is","are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape:","else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node)","[] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do","stringwhich corresponds to the variable, index combination. Parameters ---------- value : str Unique","in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this to underscore private","`Node` instance but does not belong to this graph. \"\"\" if isinstance(node, str):","this graph. \"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name","name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are node","in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name","node except Exception as ex: # pragma: no cover messages = [] interactive","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self,","#TODO: Update this to check for finalzied shape if self.shape == UNSET_SHAPE: raise",": Node This node. Raises ------ ValueError If an node with `value` already","disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\")","slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def","for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape","with `value` already exists in the associated graph. KeyError If the current name","def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","or None Shape of the output for a node. This can be a","is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs)","tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return","not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else:","\"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other,","import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import functools","= None self.op_name = op_name # Get the stack context so we can","var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val,","if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx =","other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self,","other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self,","dictionary of node names with their values. .. note:: This function modifies the","Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape","mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self,","domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional arguments which are","tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x:","@property def var(self): return self @property def name(self): \"\"\"str : Unique name of","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph)","list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if","name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by replacing all node","self): raise RuntimeError(f\"node '{node}' does not belong to {self} graph, instead belongs to\"","single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self,","isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph)","the stack context so we can report where the node was defined self._stack","name.append(key) name = self.var.name + \"[\" + \"][\".join(name) + \"]\" if name in","update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key","or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else:","from numbers import Integral, Rational, Real import contextlib import traceback import uuid import","def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def","stack = [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access","None and name not in g.nodes: g = g.graph if name in g.nodes:","*args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node.","DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for","else: name.append(str(k)) else: name.append(key) name = self.var.name + \"[\" + \"][\".join(name) + \"]\"","nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index)","else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name):","---------- args : tuple Positional arguments passed to the `_evaluate` method. name :","`func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func :","nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len)","numpy as np import importlib from .graph import Graph from .domain import Domain","values. Parameters ---------- context : dict Normalised context in which to evaluate the","cover messages = [] interactive = False if isinstance(node, Node) or not is_iterable(node):","other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other,","np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1:","nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift)","nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_)","def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","__len__(self): #TODO: Update this to check for finalzied shape if self.shape == UNSET_SHAPE:","to the target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if","slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def","import functools from numbers import Integral, Rational, Real import contextlib import traceback import","= False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var,","found in the associated graph. \"\"\" # TODO: Need a way to check","getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other):","node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name =","Shape of the output for a node. This can be a tuple of","Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg","if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name]","arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name]","nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile)","context, callback=callback) for fetch in fetches] return values[0] if single else tuple(values) def","and name not in g.nodes: g = g.graph if name in g.nodes: return","np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape)","self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral):","other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other):",": list Sequence of positional arguments passed to `func`. kwargs : dict Mapping","op_name # Get the stack context so we can report where the node","nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq)","new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k,","Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x:","node has a default value to use for execution, it can be set","node. callback : callable or None Callback to be evaluated when an node","cannot be found in the associated graph. \"\"\" name = name or uuid.uuid4().hex","return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def","== 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value","d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and","keywoard arguments passed to the target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"]","= nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ =","shape finalized\" to work self._shape = UNSET_SHAPE else: shapes = [] for dim","in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return","[] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self,","__rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self,","nodes. callback : callable or None Callback to be evaluated when an node","var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self,","def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv,","var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self,","var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape)","node raise ValueError(f\"'{node}' is not an `Node` instance or node name\") def instantiate_graph(self,","or not is_iterable(node): node = [node] for n in node: stack = []","Node The multi-dimensional variable used for indexing into. idx : tuple Tuple of","cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def","index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if","np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret","__rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self,","class Node(object): \"\"\" Base class for nodes. Parameters ---------- args : tuple Positional","= True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if","defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls,","name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph","other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self,","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return","k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node,","getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv","% self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if","the node is executed. args : list Sequence of positional arguments passed to","s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape =","== UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0]","mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_","return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def","---------- context : dict[Node or str, object] Context whose keys are node instances","fname: continue # Stop tracing at the last interactive cell if interactive and","= self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape)","domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\",","__eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph)","= [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str):","name of the node cannot be found in the associated graph. \"\"\" #","setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_","Node One or more `Node` instances or names to evaluate. context : dict","= [fetches] single = True elif isinstance(fetches, Sequence): single = False else: raise","np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0]","else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape","f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and not all([(idx_val - 1)","op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var","passed to the target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"]","tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_","f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda","or str or Node One or more `Node` instances or names to evaluate.","self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\"","to be evaluated when an node is evaluated. kwargs : dict Additional context","__rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self,","slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def","return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def","else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx])","def op_name(self): \"\"\" str : Operation name which describes the node functionality. \"\"\"","and keyword arguments `kwargs`. Parameters ---------- func : callable Function to call when","*args): assert self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name def","---------- fetches : list[str or Node] or str or Node One or more","def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or more nodes","------- values : Node or tuple[object] Output of the nodes given the context.","if node in context: raise ValueError(f\"duplicate value for node '{node}'\") context[node] = value","= fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s`","isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx)","default graph. Parameters ---------- graph : Node or None Graph to return or","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return or_(self,","super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self): return","# Evaluate the node callback = callback or _noop_callback with callback(self, context): if","slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def","for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def","that all `dependencies` are executed before any nodes in this scope. Parameters ----------","the nodes given the context. Raises ------ ValueError If `fetches` is not an","if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if","op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name","nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec)","Inheriting nodes should implement this function to evaluate the node. \"\"\" return self(*args,","or None If a node has a default value to use for execution,","graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph =","no cover messages = [] interactive = False if isinstance(node, Node) or not","---------- target : callable function to evaluate the node args : tuple positional","not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index {self} with variable shape","callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the node given a context. Parameters","parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args]","bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_","a in self.args] @property def shape(self): \"\"\" tuple : Shape of the output","keyed by variable name. Returns ------- values : Node or tuple[object] Output of","self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","RuntimeError(f\"node '{node}' does not belong to {self} graph, instead belongs to\" f\" {node.graph}\")","ValueError(f\"duplicate unequal value for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\",","Parameters ---------- target : cal The multi-dimensional variable used for indexing into. idx","__lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other,","return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if","for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are greater","slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self,","callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key: partial(value) for","call(func, *args, **kwargs): \"\"\" Call `func` with positional arguments `args` and keyword arguments","return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name,","data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name of the node and","making sure to replicate the new name with a unique stringwhich corresponds to","(\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if","isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1","def var(self): var, index_list = self.args return var def set_name(self, name): \"\"\" Set","args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this","= target self.added_attrs += [\"domain\", \"target\"] @property def target(self): return self._target @target.setter def","context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self,","return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph)","op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter","(1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices =","\"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not","name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if","def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph)","(\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return","class for nodes. Parameters ---------- args : tuple Positional arguments passed to the","dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret =","op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return","val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2):","finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for","def target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def","hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_","integers or parameter node names. \"\"\" return self._shape @property def var(self): return self","def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate","def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index {self} with variable shape {self.var.shape}\")","integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def","self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif","return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()):","\"\"\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a","`None`, this is the top-level graph. op_name : str Operation name which describes","nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller)","len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape,","else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx","\"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx","= traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this node.","enable \"is shape finalized\" to work self._shape = UNSET_SHAPE else: shapes = []","**kwargs): # pylint: disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"],","other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self)","if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return","node with `value` already exists in the associated graph. KeyError If the current","(\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if","self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args","stack context so we can report where the node was defined self._stack =","['start', 'stop', 'step']]) return node except Exception as ex: # pragma: no cover","= tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1","index values. Parameters ---------- var : Node The multi-dimensional variable used for indexing","!= \"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key,","Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple):","self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self,","\"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not","the `_evaluate` method. name : str or None Name of the node or","from polymath import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict,","add the node to the graph self._name = None self.name = name or","other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other,","`kwargs`. Parameters ---------- func : callable Function to call when the node is","creating nodes from functions. \"\"\" # This is called when the decorator is","in fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i","and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *,","other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","more than one value for any node. ValueError If `context` is not a","object] Normalized context whose keys are node instances. Raises ------ ValueError If the","= [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None,","instances or names. kwargs : dict[str, object] Additional context information keyed by variable","index {self} with variable shape {self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0]","self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other):","[] self._succesors = [] self.args = args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs","other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","\"Failed to evaluate node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex","c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch,","graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return","= nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ =","= self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and","i in c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values","tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a for a","Failed to evaluate an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node","Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph)","len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key,","return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is","self.graph while g is not None and name not in g.nodes: g =","other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self,","belong to {self} graph, instead belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}'","slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target","the associated graph. \"\"\" # TODO: Need a way to check if the","and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf","raise TypeError(f\"Variable {var} with type {type(var)} is not a list or numpy array,","for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\",","self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\"domain\"]","for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif","modified. Parameters ---------- context : dict[Node or str, object] Context whose keys are","**kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call `func` with positional","self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self,","f\"New: {self.args}\") if self.graph: graph = self.graph if self._name is not None and","None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"]","Do not capture any internal stack traces fname = frame.filename if 'polymath' in","Parameters ---------- fetches : list[str or Node] or str or Node One or","variable shape {self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var,","np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape} in {self.var.name} -","\"index\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other,","and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or","nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr)","context): if self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else:","node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for nodes. Parameters ----------","in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this to check for","else: raise TypeError(f\"Shape value must be placeholder or integer value for {self.name}\\n\" f\"\\tDim:","slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except Exception as","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph)","self.added_attrs} # Evaluate the node callback = callback or _noop_callback with callback(self, context):","in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\")","to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the","**kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\")","name : str or None Name of the node or `None` to use","in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret =","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph)","= nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ =","value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False","__rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph)","not capture any internal stack traces fname = frame.filename if 'polymath' in fname:","ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_","frame.filename if 'polymath' in fname: continue # Stop tracing at the last interactive","= nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ =","op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name,","op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value","divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_","ValueError If an node with `value` already exists in the associated graph. KeyError","def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow,","ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key),","g = self.graph while g is not None and name not in g.nodes:","single = False else: raise ValueError(\"`fetches` must be an `Node` instance, node name,","@property def op_name(self): \"\"\" str : Operation name which describes the node functionality.","for i in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other)","nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_)","normalized_context : dict[Node, object] Normalized context whose keys are node instances. Raises ------","the context. Raises ------ ValueError If `fetches` is not an `Node` instance, node","= [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in","be evaluted before evaluating any nodes defined in this scope. \"\"\" # Add","def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt,","delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem","__rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self,","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other):","key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in","node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values of a","[] self._args = [] self._predeecessors = [] self._succesors = [] self.args = args",": Node or None Graph to return or `None` to use the default","graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context, callback=None): \"\"\"","\"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other,","return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph)","else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise","list Sequence of positional arguments passed to `func`. kwargs : dict Mapping of","list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False):","_is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var,","slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain","__gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self,","op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if","return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return","sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_","a list or numpy array, and cannot be sliced for {self.name}\") elif isinstance(var,","= tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var,","_is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx","used with arguments if target is None: return functools.partial(nodeop, **kwargs) # This is","without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper","ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self)","__rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return","slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def","attributes and subgraphs of a node. \"\"\" return node_hash(self) def find_node(self, name): g","\"\"\" Instantiate nodes by retrieving the node object associated with the node name.","This is called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args,","else: raise ValueError(\"`fetches` must be an `Node` instance, node name, or a \"","def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","of this node. If graph is `None`, this is the top-level graph. op_name","arguments which are used for executing this node. \"\"\" return tuple(self._args) @property def","return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key,","multi-dimensional variable used for indexing into. idx : tuple Tuple of either integer","indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var =","reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if","evaluate the node. \"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context,","other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other):","_evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return","tuple or None Shape of the output for a node. This can be","{var} with type {type(var)} is not a list or numpy array, and cannot","hash of all attributes and subgraphs of a node. \"\"\" return node_hash(self) def","nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos)","Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self)","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return sub(self, other,","other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self,","# pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node object associated with the","else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in","i in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def","than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex","return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self):","the node given the context. \"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context,","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other):","self : Node This node. Raises ------ ValueError If an node with `value`","__floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","stack traces fname = frame.filename if 'polymath' in fname: continue # Stop tracing","self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return","does not belong to {self} graph, instead belongs to\" f\" {node.graph}\") return node","of all attributes and subgraphs of a node. \"\"\" return node_hash(self) def find_node(self,","str or Node One or more `Node` instances or names to evaluate. context","implement this function to evaluate the node. \"\"\" return self(*args, context, **kwargs) @classmethod","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self,","of the node and update the graph. Parameters ---------- value : str Unique","if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self,","if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}')","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self,","self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self,","__rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior)","keyword arguments passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None):","else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self):","name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list))","__repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] =","init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names =","context is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\"`context` must","else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self)","= slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain,","tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if","= nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ =","other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self)","the context being modified. Parameters ---------- context : dict[Node or str, object] Context","being modified. Parameters ---------- fetches : list[str or Node] or str or Node","pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_","nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_)","other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self)","self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None:","name. RuntimeError If `node` is an `Node` instance but does not belong to","= [] self._succesors = [] self.args = args if \"name\" in kwargs: kwargs.pop(\"name\")","(\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if","(slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx =","= nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ =","if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if","= nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ =","str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_","be evaluated when an node is evaluated. kwargs : dict Additional context information","either integer values or index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs):","name which describes the node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name):","slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var,","and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key,","the values. Parameters ---------- context : dict Normalised context in which to evaluate","# Get the stack context so we can report where the node was","node instances. .. note:: This function modifies the context in place. Use :code:`context=context.copy()`","aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self,","self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph):","return n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \"\"\"","name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True","Unique name of the node\"\"\" return self._name @property def op_name(self): \"\"\" str :","evaluate(self, context, callback=None): \"\"\" Evaluate the node given a context. Parameters ---------- context","the top-level graph. op_name : str Operation name which describes the node functionality.","has indices which are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape:","Node): if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}'","Node) or not is_iterable(node): node = [node] for n in node: stack =","self.args return var def set_name(self, name): \"\"\" Set the name for a variable","nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map)","is not a mapping. \"\"\" if context is None: context = {} elif","name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph:","Instantiate nodes by retrieving the node object associated with the node name. Parameters","isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return","else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return","= [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access #","a default value to use for execution, it can be set using `value`.","np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) !=","= (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single","if self.shape == UNSET_SHAPE: return False for s in self.shape: if not isinstance(s,","nodes. \"\"\" def __init__(self, target, *args, **kwargs): if \"domain\" in kwargs: domain =","== 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int,","name = name or uuid.uuid4().hex # TODO: Need a way to check if","OrderedDict() self.shape = shape or tuple([]) # Get a list of all dependencies","type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_","isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape):","other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","node name, or a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches =","name not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\")","all attributes and subgraphs of a node. \"\"\" return node_hash(self) def find_node(self, name):","= nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ =","\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active graph","RuntimeError If `node` is an `Node` instance but does not belong to this","---------- var : Node The multi-dimensional variable used for indexing into. idx :","other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self)","= Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif","isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_","isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args)","return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph)","hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_","Callback to be evaluated when an node is evaluated. kwargs : dict Additional","not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem","= self.graph while g is not None and name not in g.nodes: g","\"\"\" return self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self,","slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target =","= False if isinstance(node, Node) or not is_iterable(node): node = [node] for n","Additional context information keyed by variable name. Returns ------- values : Node or","a node. This can be a tuple of integers or parameter node names.","dependencies of this node and discard the values. Parameters ---------- context : dict","\"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context:","in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other):","func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters ---------- target","res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)","\"index\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other,","domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key)","for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single","help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_","self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op,","return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph)","np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None,","else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list =","open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_","other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","tuple of integers or parameter node names. \"\"\" return self._shape @property def var(self):","n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def","def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other):","if isinstance(node, Node): if node.name not in self.nodes and (node.graph != self): raise","return self._shape @property def var(self): return self @property def name(self): \"\"\"str : Unique","def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","__neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self,","= nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv =","(func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d))","any nodes defined in this scope. \"\"\" # Add dependencies to the graph","index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs): if \"domain\" in kwargs: domain","Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches : list[str","False if isinstance(node, Node) or not is_iterable(node): node = [node] for n in","slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def","if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and","= nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem =","= op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__","= tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx,","raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph","nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf)","Operation name which describes the node functionality. value : Any or None If","= self.nodes[name] if node in context: raise ValueError(f\"duplicate value for node '{node}'\") context[node]","default graph. Raises ------ ValueError If no `Graph` instance can be obtained. \"\"\"","is evaluated. Returns ------- value : object Output of the node given the","return \"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None,","Stop tracing at the last interactive cell if interactive and not fname.startswith('<'): break","else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","or str Node instance or name of an node. Returns ------- instantiated_node :","order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var,","function to evaluate the node args : tuple positional arguments passed to the","if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context,","Integral, Rational, Real import contextlib import traceback import uuid import numpy as np","explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate","shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph,","other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def","other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self)","enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are greater than the variable shape:\\n\"","_graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def","Set the name of the node and update the graph. Parameters ---------- value","__add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret","f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else","**kwargs): \"\"\" Instantiate a graph by replacing all node names with node instances.","isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key),","node. If graph is `None`, this is the top-level graph. op_name : str","not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)} is not a","f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name = [] if","tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key]","the node. Returns ------- self : Node This node. Raises ------ ValueError If","{name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this to check","[] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str): for","slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def","{self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name is not None","called when the decorator is used with arguments if target is None: return","def find_node(self, name): g = self.graph while g is not None and name","if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else: out_shape","self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the node to","name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\")","executing this node. \"\"\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a,","None Callback to be evaluated when an node is evaluated. kwargs : dict","__getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key,","__floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other,","passed to the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size","= nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ =","nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge)","variable index, making sure to replicate the new name with a unique stringwhich","domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var,","= self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name =","self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = []","else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property","= nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ =","node = self.nodes[name] if node in context: raise ValueError(f\"duplicate value for node '{node}'\")","in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack traces","add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self,","return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args =","self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if","= self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else:","node names. graph : Node or None Parent graph of this node. If","value : str Unique name of the node. Returns ------- self : Node","a random, unique identifier. shape : tuple or None Shape of the output","modifies the context in place. Use :code:`context=context.copy()` to avoid the context being modified.","if c in fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\"","max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_","else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x],","5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None,","def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def","to be evaluated when an node is evaluated. \"\"\" for node in self.dependencies:","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return","\"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None,","is called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner):","hash of a particular node. The default hash returns an object id, whereas","list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args,","name(self): \"\"\"str : Unique name of the node\"\"\" return self._name @property def op_name(self):","node object associated with the node name. Parameters ---------- node : Node or","should implement this function to evaluate the node. \"\"\" return self(*args, context, **kwargs)","self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return","variable used for indexing into. idx : tuple Tuple of either integer values","\"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate","placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes)","\"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not","else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","instance by returning the explicitly given graph or using the default graph. Parameters","isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if","context in which to evaluate the node. callback : callable or None Callback","is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self","None Callback to be evaluated when an node is evaluated. Returns ------- value","tuple : Shape of the output for a node. This can be a","context = self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and","kwargs : dict Keyword arguments passed to the `_evaluate` method. \"\"\" _graph_stack =","getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key,","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph)","= nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge =","Graph to return or `None` to use the default graph. Raises ------ ValueError","if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs)","self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self,","evaluated. kwargs : dict Additional context information keyed by variable name. Returns -------","ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self):","other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self,","kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target","target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\"","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return","Get a list of all dependencies relevant to this node self.dependencies = []","getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice,","(\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return","return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key,","np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions","target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self,","functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name","place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches :","contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv","while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self","value for any node. ValueError If `context` is not a mapping. \"\"\" if","shape(self): \"\"\" tuple : Shape of the output for a node. This can","def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node or constant given a","if isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict): return","(\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if","__lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return add(other,","other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other):","# TODO: Change in order to enable \"is shape finalized\" to work self._shape","discard the values. Parameters ---------- context : dict Normalised context in which to","particular node. The default hash returns an object id, whereas this function returns","\"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other,","by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys","when the node is executed. args : list Sequence of positional arguments passed","kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([]) #","fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__","func_hash(self): \"\"\" This returns the functional hash of a particular node. The default","or None Parent graph of this node. If graph is `None`, this is","f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self,","var for var index {self} with variable shape {self.var.shape}\") return True else: return","{self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain","key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name","f\"New: {self.args}\") if self.graph: graph = self.graph if self._name and self._name in graph.nodes:","{self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name =","list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other):","target : callable function to evaluate the node args : tuple positional arguments","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other):","value in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\"duplicate value","graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return","TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num","isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict): return {partial(key):","len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) !=","self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return","def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \"\"\" This returns","def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift,","name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self,","# TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph =","self._name = name return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies","def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift,","else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self,","None Shape of the output for a node. This can be a tuple","__truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self)","context : dict or None Context in which to evaluate the nodes. callback","\"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value =","name) else: graph.nodes[name] = self self._name = name return self def __getitem__(self, key):","= nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ =","context, **kwargs): \"\"\" Evaluate an node or constant given a context. \"\"\" Node.evaluated_nodes","keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node =","cannot be sliced for {self.name}\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape)","nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all)","list)): raise TypeError(f\"Variable {var} with type {type(var)} is not a list or numpy","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return","inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift","or `None` to use the default graph. Raises ------ ValueError If no `Graph`","return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self,","= [partial(arg) for arg in self.args] kwargs = {key: partial(value) for key, value","node and discard the values. Parameters ---------- context : dict Normalised context in","context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom =","**kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\"","def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" %","Set the name for a variable index, making sure to replicate the new","\"\"\" Set the name of the node and update the graph. Parameters ----------","self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs +=","graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self,","nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub)","def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred)","def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1,","slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx","def set_name(self, name): \"\"\" Set the name for a variable index, making sure","when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target,","= nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ =","i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var","domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"] @property def target(self): return","other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self)","(\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if","node args : tuple positional arguments passed to the target kwargs : dict","kwargs : dict keywoard arguments passed to the target \"\"\" def __init__(self, target,","add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE:","\"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx =",": tuple Positional arguments passed to the `_evaluate` method. name : str or","`value`. kwargs : dict Keyword arguments passed to the `_evaluate` method. \"\"\" _graph_stack","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self,","return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname)","graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ =","nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property)","or more `Node` instances or names to evaluate. context : dict or None","name): \"\"\" Set the name of the node and update the graph. Parameters","index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior","!= len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in","\"\"\" return self._shape @property def var(self): return self @property def name(self): \"\"\"str :","return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def","if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] =","nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv)","\"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not","and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index {self} with variable","nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr)","= nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ =","n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]),","when an node is evaluated. Returns ------- value : object Output of the","in fname: continue # Stop tracing at the last interactive cell if interactive","def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real,","slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def","nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem)","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph)","= nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert =","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph)","nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir)","by retrieving the node object associated with the node name. Parameters ---------- node","Parameters ---------- context : dict[Node or str, object] Context whose keys are node","Raises ------ ValueError If `fetches` is not an `Node` instance, node name, or","(str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single =","self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else:","kwargs = {key: partial(value) for key, value in self.kwargs.items() if key not in","return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other):","at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args,","\"\"\" Evaluate an node or constant given a context. \"\"\" Node.evaluated_nodes += 1","Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name)","slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs:","func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are executed","in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for","= nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter =","len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_","= res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other,","the top-level graph. \"\"\" return self._graph def preds(self): return self._preds def succs(self): return","elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) ==","arguments passed to `func`. kwargs : dict Mapping of keyword arguments passed to","context. Parameters ---------- context : dict Normalised context in which to evaluate the","other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__,","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other,","associated graph. KeyError If the current name of the node cannot be found","the default graph. Raises ------ ValueError If no `Graph` instance can be obtained.","def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s'","= nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ =","to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that","import numpy as np import importlib from .graph import Graph from .domain import","Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_","\"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s` defined at:\\n\\n%s\" % (n, stack)","self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"] @property def","np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)} is","if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other,","= nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ =","0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch","node is not equal to the current ndoe as ewll if self.graph and","self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx","else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","node. Returns ------- self : Node This node. Raises ------ ValueError If an","node is executed. args : list Sequence of positional arguments passed to `func`.","Keyword arguments passed to the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack =","for s in self.shape: if not isinstance(s, Integral): return False return True def","self.dependencies = [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) #","@property def args(self): \"\"\" tuple : Positional arguments which are used for executing","yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return","= f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def","succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def","nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this to check for finalzied shape","not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k))","def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv,","EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if","return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def","callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\": context[self] = self.value","self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape","dict Normalised context in which to evaluate the node. callback : callable or","% \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes","ValueError If `node` is not an `Node` instance or an node name. RuntimeError","other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","def argnames(self): return [a.name if isinstance(a, Node) else a for a in self.args]","ValueError(\"`context` must be a mapping.\") nodes = list(context) # Add the keyword arguments","f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not","function returns a hash of all attributes and subgraphs of a node. \"\"\"","exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_","domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args,","self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if","isinstance(a, Node) else a for a in self.args] @property def shape(self): \"\"\" tuple","for creating nodes from functions. \"\"\" # This is called when the decorator","@property def name(self): \"\"\"str : Unique name of the node\"\"\" return self._name @property","import importlib from .graph import Graph from .domain import Domain from .util import","a context. Parameters ---------- context : dict Normalised context in which to evaluate","\"\"\" Evaluate the node given a context. Parameters ---------- context : dict Normalised","truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self,","in fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def","we can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other,","= nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ =","graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def __getitem__(self,","def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift,","min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_","are node instances or names. kwargs : dict[str, object] Additional context information keyed","update the graph. Parameters ---------- value : str Unique name of the node.","are node instances. Raises ------ ValueError If the context specifies more than one","def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return","= self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\"","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return","if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain =","= self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs)","or numpy array, and cannot be sliced for {self.name}\") elif isinstance(var, list): var","if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name,","del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help)","for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr","= \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)]","name): g = self.graph while g is not None and name not in","node is evaluated. Returns ------- value : object Output of the node given","instance or name of an node. Returns ------- instantiated_node : Node Node instance.","(\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if","(\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if","Positional arguments passed to the `_evaluate` method. name : str or None Name","\"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif","= nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne =","return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an","\"\"\" Node representing values of a variable corresponding to input index values. Parameters","other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","in the associated graph. \"\"\" name = name or uuid.uuid4().hex # TODO: Need","neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def","indexing into. idx : tuple Tuple of either integer values or index/index_op nodes.","if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom","the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph)","contextlib import traceback import uuid import numpy as np import importlib from .graph","= (1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices","return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph)","== \"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value","@property def shape(self): \"\"\" tuple : Shape of the output for a node.","% (self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on a","@classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node or constant given","@property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph","disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters ---------- target : callable function","to the `_evaluate` method. name : str or None Name of the node","1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices","_is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index))","kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2:","for attr in ['start', 'stop', 'step']]) return node except Exception as ex: #","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return xor(self, other,","Parameters ---------- func : callable Function to call when the node is executed.","= nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint =","slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def","find_node(self, name): g = self.graph while g is not None and name not","\"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not","return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph)","\"\"\" Evaluate one or more nodes given a dictionary of node names with","var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain","*args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d","nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate)","**kwargs): \"\"\" Decorator for creating nodes from functions. \"\"\" # This is called","\"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not","list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) >","be sliced for {self.name}\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) !=","slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def","frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack","Parameters ---------- var : Node The multi-dimensional variable used for indexing into. idx","# Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return","Tuple of either integer values or index/index_op nodes. \"\"\" def __init__(self, target, *args,","in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain =","If graph is `None`, this is the top-level graph. \"\"\" return self._graph def","**kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are executed before","evaluated. Returns ------- value : object Output of the node given the context.","dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return","to this graph. \"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if","import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence,","\"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not","return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph)","pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n =","domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list =","or uuid.uuid4().hex # TODO: Need a way to check if the existing node","lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod","node given a context. Parameters ---------- context : dict Normalised context in which","list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_","indices)) res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if","invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_","context, **kwargs): \"\"\" Instantiate a graph by replacing all node names with node","if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes","isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must","def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in kwargs:","slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def","(len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape)","{out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and not all([(idx_val -","existing node is not equal to the current ndoe as ewll if self.graph","node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by replacing all","scope. Parameters ---------- dependencies : list Sequence of nodes to be evaluted before","else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","**kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\"","Parameters ---------- node : Node or str Node instance or name of an","node, context, **kwargs): \"\"\" Evaluate an node or constant given a context. \"\"\"","callback(self, context): if self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs)","must be a mapping.\") nodes = list(context) # Add the keyword arguments for","rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth","else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs)","from .graph import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable,","__ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx","self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return","Unique name of the node. Returns ------- self : Node This node. Raises","# pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message =","k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes =",": str Unique name of the node. Returns ------- self : Node This","Add the keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node)","elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0:","= self.var.name + \"[\" + \"][\".join(name) + \"]\" if name in self.graph.nodes: return","nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals)","corresponding to input index values. Parameters ---------- var : Node The multi-dimensional variable","builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import functools from","a tuple of integers or parameter node names. graph : Node or None","cal The multi-dimensional variable used for indexing into. idx : tuple Tuple of","else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","(k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items())","Node or str Node instance or name of an node. Returns ------- instantiated_node","more `Node` instances or names to evaluate. context : dict or None Context","lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self,","shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if","\"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or","kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target =","= nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ =","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return","is `None`, this is the top-level graph. op_name : str Operation name which","Mapping): raise ValueError(\"`context` must be a mapping.\") nodes = list(context) # Add the","context def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or more","nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip)","node or `None` to use a random, unique identifier. shape : tuple or","*, callback=None, **kwargs): \"\"\" Evaluate one or more nodes given a dictionary of","return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should implement this","x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape)","other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return","(list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else:","------- value : object Output of the node given the context. \"\"\" #","other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","= nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift =","node and add the node to the graph self._name = None self.name =","context, callback=None): \"\"\" Evaluate the dependencies of this node and discard the values.","# TODO: Need a way to check if the existing node is not","for arg in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items()","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other):","kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this to underscore private variable","name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key,","target self.added_attrs += [\"domain\", \"target\"] @property def target(self): return self._target @target.setter def target(self,","the node cannot be found in the associated graph. \"\"\" name = name","names. \"\"\" return self._shape @property def var(self): return self @property def name(self): \"\"\"str","elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)} is not","ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\"","= kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs:","var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and","graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_","not a list or numpy array, and cannot be sliced for {self.name}\") elif","object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_","the name for a variable index, making sure to replicate the new name","abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_","nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice)","\"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not","isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture","graph=None): \"\"\" Ensure that all `dependencies` are executed before any nodes in this","\"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key,","be placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape =","**kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"] @property def target(self): return self._target","if node in context: raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node] =","= len(self) for i in range(num): yield self[i] def __eq__(self, other): return hash(self)","be set using `value`. kwargs : dict Keyword arguments passed to the `_evaluate`","in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback)","self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self,","keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose","Returns ------- value : object Output of the node given the context. \"\"\"","value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is","args : tuple Positional arguments passed to the `_evaluate` method. name : str","len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else:","does not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with","if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e)","def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape ==","graph instance by returning the explicitly given graph or using the default graph.","str Unique name of the node. Returns ------- self : Node This node.","self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other):","name of the node. Returns ------- self : Node This node. Raises ------","node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one","'polymath' in fname: continue # Stop tracing at the last interactive cell if","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return le(self,","float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape","= nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul =","return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph)","nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset)","\"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not","other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other,","attr in ['start', 'stop', 'step']]) return node except Exception as ex: # pragma:","the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph","tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx,","return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call `func` with positional arguments","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other,","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return","= np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape)","an node or constant given a context. \"\"\" Node.evaluated_nodes += 1 try: if","graph of this node. If graph is `None`, this is the top-level graph.","__getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\":","name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in","for c in context: if c in fetches and c.op_name in [\"output\", \"state\",","(\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def","This can be a tuple of integers or parameter node names. \"\"\" return","None Name of the node or `None` to use a random, unique identifier.","def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod,","context in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ----------","slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx =","dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the","slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def","equal to the current ndoe as ewll if self.graph and name in self.graph.nodes:","dict Keyword arguments passed to the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack","if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the","self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other):","\"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in","= f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\"","nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float)","traces fname = frame.filename if 'polymath' in fname: continue # Stop tracing at","x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single","= nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ =","all `dependencies` are executed before any nodes in this scope. Parameters ---------- dependencies","use a random, unique identifier. shape : tuple or None Shape of the","isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else:","iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_","input index values. Parameters ---------- var : Node The multi-dimensional variable used for","of an node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError","dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ =","old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else","Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif","any nodes in this scope. Parameters ---------- dependencies : list Sequence of nodes","= nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod =","f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if","\"\"\" Evaluate the dependencies of this node and discard the values. Parameters ----------","= nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le =","in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self","\"\"\" Set the name for a variable index, making sure to replicate the","= name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the","to work self._shape = UNSET_SHAPE else: shapes = [] for dim in shape:","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph)","TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph = graph","callback : callable or None Callback to be evaluated when an node is","var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else:","nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum)","def domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional arguments which","no `Graph` instance can be obtained. \"\"\" graph = graph or Node._graph_stack[-1] return","raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\"","slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on a node. Parameters ---------- target","nodes given the context. Raises ------ ValueError If `fetches` is not an `Node`","Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in","\"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not","is the top-level graph. \"\"\" return self._graph def preds(self): return self._preds def succs(self):",": list Sequence of nodes to be evaluted before evaluating any nodes defined","and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack","their values. .. note:: This function modifies the context in place. Use :code:`context=context.copy()`","values : Node or tuple[object] Output of the nodes given the context. Raises","return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\")","self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other):","return self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield self[i]","int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_","[fetches] single = True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches`",": dict Mapping of keyword arguments passed to `func`. \"\"\" return func(*args, **kwargs)","dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_","The multi-dimensional variable used for indexing into. idx : tuple Tuple of either","graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var,","[] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float):","args : tuple positional arguments passed to the target kwargs : dict keywoard","returns an object id, whereas this function returns a hash of all attributes","__mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else",": Operation name which describes the node functionality. \"\"\" return self._op_name @op_name.setter def","val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not","elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]),","to evaluate the node. callback : callable or None Callback to be evaluated","= op_name # Get the stack context so we can report where the","range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var)","frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_","== UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral): return","def domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args return var","def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func,","value = context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal","context = {} elif not isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\")","context, callback=None): \"\"\" Evaluate the node given a context. Parameters ---------- context :","else x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx in","__init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else","the node cannot be found in the associated graph. \"\"\" # TODO: Need","== DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices","must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num =","def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return","memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter","to return or `None` to use the default graph. Raises ------ ValueError If","elif not isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\") nodes = list(context)","shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for","= self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self)","other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self)","np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does","target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in","= nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul =","if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape)","def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are executed before any nodes","op_name(self): \"\"\" str : Operation name which describes the node functionality. \"\"\" return","= functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs =","this scope. \"\"\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies)","a variable corresponding to input index values. Parameters ---------- var : Node The","other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other,","idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def var(self): var,","return node raise ValueError(f\"'{node}' is not an `Node` instance or node name\") def","deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args,","[self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context:","'{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and","shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if","kwargs : dict Mapping of keyword arguments passed to `func`. \"\"\" return func(*args,","Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 =","nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool)","sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_","def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name","ValueError(f\"'{node}' is not an `Node` instance or node name\") def instantiate_graph(self, context, **kwargs):","isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)} is not a list","\"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value","if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value","underscore private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape","other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","[] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name]","to the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name =","given a context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return","return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def","def __len__(self): #TODO: Update this to check for finalzied shape if self.shape ==","a node. Parameters ---------- target : cal The multi-dimensional variable used for indexing","return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph)","`Node` instance or an node name. RuntimeError If `node` is an `Node` instance","or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub","idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res =","= nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth =","g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def","else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx =","name with a unique stringwhich corresponds to the variable, index combination. Parameters ----------","passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure","np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx","= fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self","and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or","nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr)","callable or None Callback to be evaluated when an node is evaluated. Returns","in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name","is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape: if not","target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator","def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge,","nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem)","indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are","a particular node. The default hash returns an object id, whereas this function","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other,","= nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ =","other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self,","shape or tuple([]) # Get a list of all dependencies relevant to this","scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return","target, *args, **kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list)","integer values or index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs): if \"domain\"","op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1],","all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) >","= [node] for n in node: stack = [] if isinstance(n, Node): for","nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray)","isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1,","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return","globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_","slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def","isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float):","graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return","attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem","name) else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context,","kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain","graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):]","nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift)","context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key: partial(value)","other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","== 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) !=","key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret","`func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all","node given the context. \"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback)","if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1","interactive = False if isinstance(node, Node) or not is_iterable(node): node = [node] for","with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and not all([(idx_val","in self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}' does not belong to","self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args return var def set_name(self, name):","= Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa","*args, **kwargs): \"\"\" Call `func` with positional arguments `args` and keyword arguments `kwargs`.","whose keys are node instances. Raises ------ ValueError If the context specifies more","if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret =","__repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node representing","is not an `Node` instance, node name, or a sequence thereof. \"\"\" if","g is not None and name not in g.nodes: g = g.graph if","assert self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self,","def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape: if","message = \"Failed to evaluate node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message)","graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return","before evaluating any nodes defined in this scope. \"\"\" # Add dependencies to","= self self._name = name return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph)","= tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance by","other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self)","else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral,","`node` is not an `Node` instance or an node name. RuntimeError If `node`",": dict[Node or str, object] Context whose keys are node instances or names.","var, idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in kwargs: domain =","graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return","concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq","and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and","== hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if","= [] self.args = args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = []","Decorator for creating nodes from functions. \"\"\" # This is called when the","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other):","deque import functools from numbers import Integral, Rational, Real import contextlib import traceback","else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self,","else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","be evaluated when an node is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context,","def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903","def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift,","self._name = None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name =","not an `Node` instance, node name, or a sequence thereof. \"\"\" if isinstance(fetches,","(n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None,","is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0]","# pylint: disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list)","def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow,","if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname)","val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)","# This is called when the decorator is used with arguments if target","c in context: if c in fetches and c.op_name in [\"output\", \"state\", \"temp\"]:",": dict keywoard arguments passed to the target \"\"\" def __init__(self, target, *args,","else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any","on a node. Parameters ---------- target : cal The multi-dimensional variable used for","@shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph)","== old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx):","!= DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var","self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self):","node '{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"]","if context is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\"`context`","{key}\") name = f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name} not in","self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name]","report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\"","variable corresponding to input index values. Parameters ---------- var : Node The multi-dimensional","or Node] or str or Node One or more `Node` instances or names","f\"Key: {key}\") name = f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name} not","nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter)","= nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ =","domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args","return or `None` to use the default graph. Raises ------ ValueError If no","private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape =","be found in the associated graph. \"\"\" name = name or uuid.uuid4().hex #","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other):","return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n","return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self,","self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >=","__getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the","nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod)","return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def","to evaluate an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing","ValueError(f\"Invalid shape var for var index {self} with variable shape {self.var.shape}\") return True","self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in","or len(shape) == 0: # TODO: Change in order to enable \"is shape","`_evaluate` method. name : str or None Name of the node or `None`","list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs):","__lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other,","tracing at the last interactive cell if interactive and not fname.startswith('<'): break #","op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return","= nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift =","be a tuple of integers or parameter node names. graph : Node or","a node has a default value to use for execution, it can be","work self._shape = UNSET_SHAPE else: shapes = [] for dim in shape: if","**kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should implement","== (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res","in order to enable \"is shape finalized\" to work self._shape = UNSET_SHAPE else:","\"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other,","or None Graph to return or `None` to use the default graph. Raises","= [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim,","arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx =","def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other,","self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,)","retrieving the node object associated with the node name. Parameters ---------- node :","import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object):","n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \"\"\" This","if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this to","node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the node","self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def","init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0:","graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self):","f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the","tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for element in","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph)","__abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self,","isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain,","name = f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name}","instance or an node name. RuntimeError If `node` is an `Node` instance but","callable Function to call when the node is executed. args : list Sequence","return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def","context : dict[Node or str, object] Context whose keys are node instances or","= self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name)","for {self.name}\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and","graph is `None`, this is the top-level graph. \"\"\" return self._graph def preds(self):","str, object] Context whose keys are node instances or names. kwargs : dict[str,","Parameters ---------- dependencies : list Sequence of nodes to be evaluted before evaluating","__ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self,","Raises ------ ValueError If `node` is not an `Node` instance or an node","self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class","tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)):","\"\"\" This returns the functional hash of a particular node. The default hash","list does not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name}","= slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name,","= self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def","an node with `value` already exists in the associated graph. KeyError If the","set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat","self.graph: graph = self.graph if self._name is not None and self._name in graph.nodes:","by returning the explicitly given graph or using the default graph. Parameters ----------","nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type)","self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self, key,","name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if","not equal to the current ndoe as ewll if self.graph and name in","other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other):","value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors = []","\"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name)","eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_","shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def","matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other):","isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0: #","domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return","= nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt =","nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift)","self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else:","other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self)","stateless functions. Parameters ---------- target : callable function to evaluate the node args","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other,","isinstance(node, Node) or not is_iterable(node): node = [node] for n in node: stack","the node given a context. Parameters ---------- context : dict Normalised context in","round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_","if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid","polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`, this is","self.args] @property def shape(self): \"\"\" tuple : Shape of the output for a","be an `Node` instance, node name, or a \" \"sequence thereof.\") fetches =","domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self,","functools.partial(nodeop, **kwargs) # This is called when the decorator is used without arguments","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self,","= nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf =","given the context. Raises ------ ValueError If `fetches` is not an `Node` instance,","nodes from functions. \"\"\" # This is called when the decorator is used","graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return","Node instance or name of an node. Returns ------- instantiated_node : Node Node","isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx] for","shape or len(shape) == 0: # TODO: Change in order to enable \"is","If the current name of the node cannot be found in the associated","True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var,","@property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None","uuid.uuid4().hex # TODO: Need a way to check if the existing node is","parameter node names. graph : Node or None Parent graph of this node.","= UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim, (Node,","self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property","[key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple,","more nodes given a dictionary of node names with their values. .. note::","This can be a tuple of integers or parameter node names. graph :","if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing:","len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from functions. \"\"\" #","Node This node. Raises ------ ValueError If an node with `value` already exists",">= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C')","if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}' does",": dict[str, object] Additional context information keyed by variable name. Returns ------- normalized_context","var : Node The multi-dimensional variable used for indexing into. idx : tuple","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self,","1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1","return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def __len__(self):","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return xor(self,","None Parent graph of this node. If graph is `None`, this is the","or using the default graph. Parameters ---------- graph : Node or None Graph","s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray)","= nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ =","(\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if","nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_ = nodeop(importlib.import_module)","graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self,","the output for a node. This can be a tuple of integers or","indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow","id(self) def func_hash(self): \"\"\" This returns the functional hash of a particular node.","= nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ =","by variable name. Returns ------- values : Node or tuple[object] Output of the","name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\")","other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other,","self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self,","\"\"\" Obtain the currently active graph instance by returning the explicitly given graph","else: shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim)","_is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for nodes. Parameters ---------- args :","\"sequence thereof.\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs)","operator from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import","identifier. shape : tuple or None Shape of the output for a node.","from functions. \"\"\" # This is called when the decorator is used with","Node or None Parent graph of this node. If graph is `None`, this","dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = []","= Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes","tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and","return [partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value) for","nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int)","\"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other,","= nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter =","self.target = target self.added_attrs += [\"domain\", \"target\"] @property def target(self): return self._target @target.setter","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self,","ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos","(self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions.","given a context. Parameters ---------- context : dict Normalised context in which to","isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx =","if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var,","None: return functools.partial(nodeop, **kwargs) # This is called when the decorator is used","# Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove","if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def","TypeError(f\"Variable {var} with type {type(var)} is not a list or numpy array, and","nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg)","nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul)","= kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([])","self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self,","= nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ =","def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()}","[key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if","than one value for any node. ValueError If `context` is not a mapping.","!= len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape}","1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add,","stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None,","or node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by replacing","key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return","node names with node instances. .. note:: This function modifies the context in","var(self): return self @property def name(self): \"\"\"str : Unique name of the node\"\"\"","self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def","for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i in","__xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other,","\\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from","Callback to be evaluated when an node is evaluated. Returns ------- value :","args : list Sequence of positional arguments passed to `func`. kwargs : dict","shape : tuple or None Shape of the output for a node. This","whereas this function returns a hash of all attributes and subgraphs of a","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self,","return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data):","class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values of a variable corresponding","(tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes)","ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple):","when the decorator is used with arguments if target is None: return functools.partial(nodeop,","slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var,","str : Operation name which describes the node functionality. \"\"\" return self._op_name @op_name.setter","isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches` must be an `Node` instance,","= self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value","\"\"\" Failed to evaluate an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\"","can be set using `value`. kwargs : dict Keyword arguments passed to the","None Callback to be evaluated when an node is evaluated. \"\"\" for node","graph. \"\"\" # TODO: Need a way to check if the existing node","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other,","an `Node` instance but does not belong to this graph. \"\"\" if isinstance(node,","context information keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized","False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)])","nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod)","for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\"","# Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for","target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"] @property def target(self):","(len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index","in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) ==","nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow)","def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph =","is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the","**kwargs) for c in context: if c in fetches and c.op_name in [\"output\",","names with their values. .. note:: This function modifies the context in place.","if self in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node,","\"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other,","\"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values of a variable","= self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var])","def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add,","self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key,","node functionality. value : Any or None If a node has a default","self._shape = tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change","pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed","idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key,","idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape","__call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s","def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return","\"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other,","nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max)","Node Node instance. Raises ------ ValueError If `node` is not an `Node` instance","= self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self)","and add the node to the graph self._name = None self.name = name","= False else: raise ValueError(\"`fetches` must be an `Node` instance, node name, or","keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name = [] if isinstance(key,","self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self,","UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def","or more nodes given a dictionary of node names with their values. ..","return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def","\"target\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc","**kwargs): \"\"\" Evaluate an node or constant given a context. \"\"\" Node.evaluated_nodes +=","instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node object associated","or constant given a context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node):","for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly","nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print)","function modifies the context in place. Use :code:`context=context.copy()` to avoid the context being","{Node._eval_stack}\") if not single and not all([(idx_val - 1) >= indices[-1][idx] for idx,","tuple Tuple of either integer values or index/index_op nodes. \"\"\" def __init__(self, target,","def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor,","or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None,","_flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for nodes. Parameters","'{node}' does not belong to {self} graph, instead belongs to\" f\" {node.graph}\") return","return self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args return var def set_name(self,","raise ValueError(\"`fetches` must be an `Node` instance, node name, or a \" \"sequence",": callable or None Callback to be evaluated when an node is evaluated.","for stateless functions. Parameters ---------- target : callable function to evaluate the node","self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","to replicate the new name with a unique stringwhich corresponds to the variable,","if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not","c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback)","== 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return","UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque","= nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf =","\"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other,","name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \"[\" + \"][\".join(name) +","other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other,","def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and","args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for","raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self):","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other):","+ \"][\".join(name) + \"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)):","tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph =","self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self,","name of the node\"\"\" return self._name @property def op_name(self): \"\"\" str : Operation","tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) ==","where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other):","slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name","return self._name @property def op_name(self): \"\"\" str : Operation name which describes the","def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k ==","return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph)","= target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple,","@target.setter def target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\"","return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif","context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\"",": Any or None If a node has a default value to use","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self,","names with node instances. .. note:: This function modifies the context in place.","other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other,","node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node object associated with","= value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors =","an node is evaluated. Returns ------- value : object Output of the node","**kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args = []","Graph: {self.graph}\") def __len__(self): #TODO: Update this to check for finalzied shape if","instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by replacing all node names with","not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index","nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow)","def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","\"is shape finalized\" to work self._shape = UNSET_SHAPE else: shapes = [] for","cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate","__rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","= nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub =","Get the stack context so we can report where the node was defined","for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the","a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches] single =","lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self,","not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name","truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self,","return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph)","replacing all node names with node instances. .. note:: This function modifies the","[\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name,","= self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes","def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def","other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self)","c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches]","unique stringwhich corresponds to the variable, index combination. Parameters ---------- value : str","dict[Node, object] Normalized context whose keys are node instances. Raises ------ ValueError If","top-level graph. op_name : str Operation name which describes the node functionality. value","Operation name which describes the node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self,","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other):","evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of this node and discard the","def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0]","def get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance by returning the explicitly","specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for","if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\",","self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should","evaluate the node args : tuple positional arguments passed to the target kwargs","not belong to {self} graph, instead belongs to\" f\" {node.graph}\") return node raise","graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return","else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self):","return functools.partial(nodeop, **kwargs) # This is called when the decorator is used without","context: raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node] = value if node.op_name","graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda","graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return","nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance)","node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\":","return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def","super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\", \"target\"] @property","hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_","== \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape):","KeyError If the current name of the node cannot be found in the","(\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if","If `context` is not a mapping. \"\"\" if context is None: context =","Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\"","ValueError If the context specifies more than one value for any node. ValueError","node self.dependencies = [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies)","__getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key =","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return or_(self, other,","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return le(self, other,","arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop","slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in","0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)):","\"[\" + \"][\".join(name) + \"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key,","or tuple([]) # Get a list of all dependencies relevant to this node","elif isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches` must be an `Node`","= nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv =","if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 =","hasattr(key, \"__len__\") and not isinstance(key, str): for k in key: if isinstance(k, Node):","{self.name} has indices which are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar","__sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other,","in self.args] @property def shape(self): \"\"\" tuple : Shape of the output for","the node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name","from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict)","bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_","= nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv =","cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args):","value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies,","return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph)","value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start',","\"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this to underscore","return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def","in ['start', 'stop', 'step']]) return node except Exception as ex: # pragma: no","arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node)","a context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context,","graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self):","in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name","element in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in","node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If `node`","a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node in fetches] context =","idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\"))","a variable index, making sure to replicate the new name with a unique","else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if","graph=self) return ret def is_scalar(self, val=None): if val is not None and (not","pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate","= self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom)","the context being modified. Parameters ---------- fetches : list[str or Node] or str","ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}'","return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key,","callback=None, **kwargs): \"\"\" Evaluate one or more nodes given a dictionary of node","is not None and name not in g.nodes: g = g.graph if name","else: name.append(key) name = self.var.name + \"[\" + \"][\".join(name) + \"]\" if name","Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional arguments which are used for","graph : Node or None Parent graph of this node. If graph is","nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars)","any node. ValueError If `context` is not a mapping. \"\"\" if context is","self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should implement this function","nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr)","def __iter__(self): num = len(self) for i in range(num): yield self[i] def __eq__(self,","not is_iterable(node): node = [node] for n in node: stack = [] if","else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args):","------ ValueError If an node with `value` already exists in the associated graph.","numpy array, and cannot be sliced for {self.name}\") elif isinstance(var, list): var =","= var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not","explicitly given graph or using the default graph. Parameters ---------- graph : Node","node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod","pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self,","import traceback import uuid import numpy as np import importlib from .graph import","for any node. ValueError If `context` is not a mapping. \"\"\" if context","and not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else:","if 'polymath' in fname: continue # Stop tracing at the last interactive cell","= context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal value","value : object Output of the node given the context. \"\"\" # Evaluate","graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node","def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif","the node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ ==","single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False","from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral,","def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","node_hash(self) def find_node(self, name): g = self.graph while g is not None and","0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d","for i in c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name]","[node] for n in node: stack = [] if isinstance(n, Node): for frame","= name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape):","of integers or parameter node names. \"\"\" return self._shape @property def var(self): return","val=None): if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) ==","self.added_attrs += [\"domain\", \"target\"] @property def target(self): return self._target @target.setter def target(self, fnc):","must be an `Node` instance, node name, or a \" \"sequence thereof.\") fetches","= nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ =","not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack =","- {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\")","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other,","index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on","context being modified. Parameters ---------- context : dict[Node or str, object] Context whose","name. Parameters ---------- node : Node or str Node instance or name of","= cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert","slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class","disable=protected-access # Do not capture any internal stack traces fname = frame.filename if","write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count > 0 else c.name","[\"domain\", \"target\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target =","arg in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if","op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args","callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\": context[self]","with variable shape {self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self,","graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_","nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not)","index_list = self.args return var def set_name(self, name): \"\"\" Set the name for","ValueError(f\"var_index {self.name} has indices which are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\"","\"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx =","**kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element","list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0],","(\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if","str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key)","avoid the context being modified. Parameters ---------- context : dict[Node or str, object]","xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self,","return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\" class","return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def","% (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None,","def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape):","= nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ =","\"\"\" # This is called when the decorator is used with arguments if","def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if","class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters ----------","in this scope. Parameters ---------- dependencies : list Sequence of nodes to be","nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable)","the node\"\"\" return self._name @property def op_name(self): \"\"\" str : Operation name which","an `Node` instance, node name, or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node)","self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx","collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational,","shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def","Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return","fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s` defined","single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices)","elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op,","the nodes. callback : callable or None Callback to be evaluated when an","and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape,","pragma: no cover messages = [] interactive = False if isinstance(node, Node) or","the existing node is not equal to the current ndoe as ewll if","self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape","__radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","describes the node functionality. value : Any or None If a node has","a way to check if the existing node is not equal to the","of either integer values or index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs):","if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape","isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float):","to evaluate the node args : tuple positional arguments passed to the target","__getitem__(self, key): return self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None):","not belong to this graph. \"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node,","scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self,","for element in node) if isinstance(node, list): return [partial(element) for element in node]","self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph:","__mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other,","+= 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node,",": object Output of the node given the context. \"\"\" # Evaluate all","elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self,","shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder or integer value for {self.name}\\n\"","into. idx : tuple Tuple of either integer values or index/index_op nodes. \"\"\"","Sequence of positional arguments passed to `func`. kwargs : dict Mapping of keyword","nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_)","of keyword arguments passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies,","# Choose a name for the node and add the node to the","nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str)","is None: return functools.partial(nodeop, **kwargs) # This is called when the decorator is","graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph","*args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n","Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self]","the keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node","val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and","self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self,","else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return","name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if","an node name. RuntimeError If `node` is an `Node` instance but does not","in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the node given","are executed before any nodes in this scope. Parameters ---------- dependencies : list","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self,","tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def","import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real","return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph)","Choose a name for the node and add the node to the graph","\"\"\" Instantiate a graph by replacing all node names with node instances. ..","partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs","__rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self,","return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph)","in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args,","instance. Raises ------ ValueError If `node` is not an `Node` instance or an","__pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other,","if out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices)","fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key):","= nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ =","return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)","with callback(self, context): if self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args, context=context,","= nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_ =","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return","(list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name,","Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial =","nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i in range(num):","self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors = [] self.args","def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ !=","self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else: out_shape =","top-level graph. \"\"\" return self._graph def preds(self): return self._preds def succs(self): return self._preds","or str, object] Context whose keys are node instances or names. kwargs :","\"__len__\") and not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name)","self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] =","to `func`. kwargs : dict Mapping of keyword arguments passed to `func`. \"\"\"","def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral):","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return","replicate the new name with a unique stringwhich corresponds to the variable, index","when an node is evaluated. kwargs : dict Additional context information keyed by","\"\"\" Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ----------","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self,","in the associated graph. KeyError If the current name of the node cannot","\"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args)","for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return","Parameters ---------- value : str Unique name of the node. Returns ------- self","[\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context","the functional hash of a particular node. The default hash returns an object","nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod)","_eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None,","thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif","self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name =","return False for s in self.shape: if not isinstance(s, Integral): return False return","place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context :","name.append(str(k)) else: name.append(key) name = self.var.name + \"[\" + \"][\".join(name) + \"]\" if","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self,","== 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain","raise ValueError(f\"duplicate value for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\",","**kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain","positional arguments passed to `func`. kwargs : dict Mapping of keyword arguments passed","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other,","executed. args : list Sequence of positional arguments passed to `func`. kwargs :","= nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ =","return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key","= nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ =","def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\"","sure to replicate the new name with a unique stringwhich corresponds to the","return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except Exception","a unique stringwhich corresponds to the variable, index combination. Parameters ---------- value :","in self.shape: if not isinstance(s, Integral): return False return True def set_shape(self, shape=None,","single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not","list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if","domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index,","= nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ =","in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback =","this node. \"\"\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node)","if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing:","graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name,","mapping.\") nodes = list(context) # Add the keyword arguments for node in nodes:","name not in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name]","__rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","to input index values. Parameters ---------- var : Node The multi-dimensional variable used","evaluated when an node is evaluated. kwargs : dict Additional context information keyed","op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key):","_is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var,","slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def","\"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or","of the node\"\"\" return self._name @property def op_name(self): \"\"\" str : Operation name","is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift","name or uuid.uuid4().hex # TODO: Need a way to check if the existing","other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self,","return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape =","ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph =","Normalized context whose keys are node instances. Raises ------ ValueError If the context","slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs:","str Node instance or name of an node. Returns ------- instantiated_node : Node","Positional arguments which are used for executing this node. \"\"\" return tuple(self._args) @property","graph. \"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not","__mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other,","def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv,","if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized()","Raises ------ ValueError If the context specifies more than one value for any","False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray,","floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self,","mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self,","instances. .. note:: This function modifies the context in place. Use :code:`context=context.copy()` to","\"][\".join(name) + \"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return","def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self,","== np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] ==","node = [node] for n in node: stack = [] if isinstance(n, Node):","the context specifies more than one value for any node. ValueError If `context`","shape var for var index {self} with variable shape {self.var.shape}\") return True else:","bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices))","fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in","= graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a","fetches : list[str or Node] or str or Node One or more `Node`","= None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name","and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else:","for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name","if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"):","Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are node instances.","and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self,","len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for","is not an `Node` instance or an node name. RuntimeError If `node` is","kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"],","traceback import uuid import numpy as np import importlib from .graph import Graph","else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda","return var def set_name(self, name): \"\"\" Set the name for a variable index,","single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]):","gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph","list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var,","node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs):","self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node]","kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\" in","other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self,","slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain,","= nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ =","slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def","of either integer values or index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None,","If no `Graph` instance can be obtained. \"\"\" graph = graph or Node._graph_stack[-1]","key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif","def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var,","Node(object): \"\"\" Base class for nodes. Parameters ---------- args : tuple Positional arguments","def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key,","isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and","the associated graph. KeyError If the current name of the node cannot be","ValueError(f\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice","= \"Failed to evaluate node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise","= 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes","(self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on a node.","Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0: # TODO:","def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt,","messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None,","return Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional arguments which are used","not a mapping. \"\"\" if context is None: context = {} elif not","s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d,","= shape or tuple([]) # Get a list of all dependencies relevant to","not shape or len(shape) == 0: # TODO: Change in order to enable","\"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items():","sliced for {self.name}\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape)","node. This can be a tuple of integers or parameter node names. \"\"\"","if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return","np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0:","= Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self,","if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for","can be a tuple of integers or parameter node names. \"\"\" return self._shape","\"\"\" Inheriting nodes should implement this function to evaluate the node. \"\"\" return","range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name):","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other):","**kwargs): return call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\"","nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add)","self.value = value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors","isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise","slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa","self @property def name(self): \"\"\"str : Unique name of the node\"\"\" return self._name","information keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context","ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not","uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack context so","= np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)}","`Node` instance, node name, or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for","mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self,","= tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape:","object associated with the node name. Parameters ---------- node : Node or str","nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format)","0: # TODO: Change in order to enable \"is shape finalized\" to work","graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return","or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain","if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else:","(\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if","__lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name #","classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_","def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,)","@classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args)","Sequence, deque import functools from numbers import Integral, Rational, Real import contextlib import","kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain","is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] ==","None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index))","graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self):","positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function","\"\"\" Decorator for creating nodes from functions. \"\"\" # This is called when","is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"):","node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent","self._succesors = [] self.args = args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs =","the target kwargs : dict keywoard arguments passed to the target \"\"\" def","CHange this to underscore private variable self.kwargs = kwargs self.graph = graph self._shape","slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def","(new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n)","self.shape: if not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False):","_evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single","nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin)","\"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx","{self} graph, instead belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not","in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if","Context whose keys are node instances or names. kwargs : dict[str, object] Additional","{self.graph}\") def __len__(self): #TODO: Update this to check for finalzied shape if self.shape","return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def","def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node):","return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph)","return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name","in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape","__init__(self, target, *args, **kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"],","\"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph)","*args, **kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else","list[str or Node] or str or Node One or more `Node` instances or","= nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq =","node in context: raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node] = value","keys are node instances. Raises ------ ValueError If the context specifies more than","from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs):","dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value,","\"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not","None Context in which to evaluate the nodes. callback : callable or None","__repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\"","context. \"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in","@nodeop def call(func, *args, **kwargs): \"\"\" Call `func` with positional arguments `args` and","__ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","= slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self,","Context in which to evaluate the nodes. callback : callable or None Callback","isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single","= OrderedDict() self.shape = shape or tuple([]) # Get a list of all","graph by replacing all node names with node instances. .. note:: This function","else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a list of","!= np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\"","isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple","def is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray) or","self.op_name = op_name # Get the stack context so we can report where","if isinstance(a, Node) else a for a in self.args] @property def shape(self): \"\"\"","self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other):","slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([]))","self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on a node. Parameters","a tuple of integers or parameter node names. \"\"\" return self._shape @property def","1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise","given graph or using the default graph. Parameters ---------- graph : Node or","to {self} graph, instead belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is",": list[str or Node] or str or Node One or more `Node` instances","return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph)","return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node","key not in self.added_attrs} # Evaluate the node callback = callback or _noop_callback","\"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not","as np import importlib from .graph import Graph from .domain import Domain from","Node representing multi-dimensional operations performed on a node. Parameters ---------- target : cal","tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape)","[self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def","__le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"):","other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other):","return call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" %","isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([]))","decorator is used with arguments if target is None: return functools.partial(nodeop, **kwargs) #","= np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)):","---------- context : dict Normalised context in which to evaluate the node. callback","Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors = []","other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other):","# pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters ---------- target :","other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" %","graph, instead belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not an","self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom =","str or None Name of the node or `None` to use a random,","self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes)","functions. Parameters ---------- target : callable function to evaluate the node args :","ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf","nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le)","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return","corresponds to the variable, index combination. Parameters ---------- value : str Unique name",".. note:: This function modifies the context in place. Use :code:`context=context.copy()` to avoid","name, or a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches]","other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self,",": dict Keyword arguments passed to the `_evaluate` method. \"\"\" _graph_stack = deque([None])","= nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ =","nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object)","default hash returns an object id, whereas this function returns a hash of","'%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\"","tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change in order","Sequence): single = False else: raise ValueError(\"`fetches` must be an `Node` instance, node","graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies =","random, unique identifier. shape : tuple or None Shape of the output for","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph)","self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters","passed to the `_evaluate` method. name : str or None Name of the","- 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has","partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for","in context: raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node] = value if","'{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph if","interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node","slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def","return self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred):","for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if","names to evaluate. context : dict or None Context in which to evaluate","Update this to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape`","disable=C0103,W0223 \"\"\" Node representing values of a variable corresponding to input index values.","\"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other,","other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other,","self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0:","def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e","graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return","in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx],","nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set)","e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional","__rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self,","`None` to use the default graph. Raises ------ ValueError If no `Graph` instance","Real import contextlib import traceback import uuid import numpy as np import importlib","return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def","this node. If graph is `None`, this is the top-level graph. op_name :","Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ)","avoid the context being modified. Parameters ---------- fetches : list[str or Node] or","this function returns a hash of all attributes and subgraphs of a node.","other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node):","else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val","**kwargs): \"\"\" Evaluate one or more nodes given a dictionary of node names","# Get a list of all dependencies relevant to this node self.dependencies =","{self.args}\") if self.graph: graph = self.graph if self._name is not None and self._name","= self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and c.op_name","= nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ =","list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes","= nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ =","`fetches` is not an `Node` instance, node name, or a sequence thereof. \"\"\"","isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and","\"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial","slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def","np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1,","return node except Exception as ex: # pragma: no cover messages = []","floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index","[\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count >","# pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context: raise","= slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return","self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance","the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args,","= nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ =","to enable \"is shape finalized\" to work self._shape = UNSET_SHAPE else: shapes =","if self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self]","this node self.dependencies = [] if dependencies is None else dependencies if self.graph:","cannot be found in the associated graph. \"\"\" # TODO: Need a way","\"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other,","other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self,","nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview)","map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_","[] self.args = args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] #","self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from functions. \"\"\"","= nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ =","else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","\"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not","is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint","evaluate node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages))","None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node","for a node. This can be a tuple of integers or parameter node","object id, whereas this function returns a hash of all attributes and subgraphs","__setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name of the node","if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif","in context: if c in fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name","def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa)","@staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance by returning the","if target is None: return functools.partial(nodeop, **kwargs) # This is called when the","None Graph to return or `None` to use the default graph. Raises ------","return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph)","(\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if","`dependencies` are executed before any nodes in this scope. Parameters ---------- dependencies :","mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_","nodes. Parameters ---------- args : tuple Positional arguments passed to the `_evaluate` method.",": tuple or None Shape of the output for a node. This can","if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else:","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other,","name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed","tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args)","self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the node given a","not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape,","isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder or integer value","self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self,","in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self,","self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the node","any internal stack traces fname = frame.filename if 'polymath' in fname: continue #","nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod)","an `Node` instance or an node name. RuntimeError If `node` is an `Node`","name. Returns ------- values : Node or tuple[object] Output of the nodes given","name of an node. Returns ------- instantiated_node : Node Node instance. Raises ------","else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph)","callback) if self in context: return context[self] # Evaluate the parents partial =","else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other):","nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter)","neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift","def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k:","if key not in self.added_attrs} # Evaluate the node callback = callback or","all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] #","Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str): for k in key:","tuple of integers or parameter node names. graph : Node or None Parent","{} elif not isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\") nodes =","self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other):","tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance by returning","graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s","evaluated when an node is evaluated. Returns ------- value : object Output of","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other):","> 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx,","input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_","other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other,","passed to the target kwargs : dict keywoard arguments passed to the target","\"\"\" # TODO: Need a way to check if the existing node is","value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs)","= Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph:","slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in","\"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not","ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self,","evaluate. context : dict or None Context in which to evaluate the nodes.","Parameters ---------- graph : Node or None Graph to return or `None` to","Evaluate the node given a context. Parameters ---------- context : dict Normalised context","evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\"","other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self,","return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\"","other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self)","name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s","callable function to evaluate the node args : tuple positional arguments passed to","evaluating any nodes defined in this scope. \"\"\" # Add dependencies to the","for nodes. Parameters ---------- args : tuple Positional arguments passed to the `_evaluate`",": cal The multi-dimensional variable used for indexing into. idx : tuple Tuple",": Node Node instance. Raises ------ ValueError If `node` is not an `Node`","= nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ =","len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\")","def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor,","if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def","__hash__(self): return id(self) def func_hash(self): \"\"\" This returns the functional hash of a","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other,","slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args","\"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other,","= nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift =","def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by replacing all node names","ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\"","for key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the","return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the","return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__","= c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0]","of the node cannot be found in the associated graph. \"\"\" name =","an object id, whereas this function returns a hash of all attributes and","__or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other,","= shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set):","op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom","Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other,","def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","__repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def","OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real import","other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self,","ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph =","fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self,","already exists in the associated graph. KeyError If the current name of the","object Output of the node given the context. \"\"\" # Evaluate all explicit","node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if","sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self,","(int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape","(\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if","\"\"\" return node_hash(self) def find_node(self, name): g = self.graph while g is not","out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape)","------ ValueError If `fetches` is not an `Node` instance, node name, or a","def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred,","else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","graph. Raises ------ ValueError If no `Graph` instance can be obtained. \"\"\" graph","dependencies relevant to this node self.dependencies = [] if dependencies is None else","!= len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name","other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other):",": dict Normalised context in which to evaluate the node. callback : callable","nodes by retrieving the node object associated with the node name. Parameters ----------","ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and","= nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ =","\"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not","= tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args =","element in node) if isinstance(node, list): return [partial(element) for element in node] if","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph)","graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self,","nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed)","can be obtained. \"\"\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self,","and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other):","or names. kwargs : dict[str, object] Additional context information keyed by variable name.","Evaluate an node or constant given a context. \"\"\" Node.evaluated_nodes += 1 try:","active graph instance by returning the explicitly given graph or using the default","not isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\") nodes = list(context) #","Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving","use the default graph. Raises ------ ValueError If no `Graph` instance can be","(\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if","target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\"domain\"] def","call when the node is executed. args : list Sequence of positional arguments","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return","self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter","the node name. Parameters ---------- node : Node or str Node instance or","Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If `node` is","or an node name. RuntimeError If `node` is an `Node` instance but does","graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a list","= {} elif not isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\") nodes","bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_","other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","\" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context,","else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","self.added_attrs = [] # TODO: CHange this to underscore private variable self.kwargs =","domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var","if self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name)","g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find","nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint)","or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node in fetches] context","dependencies : list Sequence of nodes to be evaluted before evaluating any nodes","in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name","__ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\"","n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args,","other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self,","= nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ =","the last interactive cell if interactive and not fname.startswith('<'): break # pragma: no","which to evaluate the nodes. callback : callable or None Callback to be","other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other,","= 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph)","\"index\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other,","def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name of the","\"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other,","isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str): for k in","the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5","fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or more nodes given a","Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies","------- self : Node This node. Raises ------ ValueError If an node with","super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_","elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index):","succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args:","for n in node: stack = [] if isinstance(n, Node): for frame in","self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name,","target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\"",": Node or tuple[object] Output of the nodes given the context. Raises ------","self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key,","continue # Stop tracing at the last interactive cell if interactive and not","other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other):","= value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in","slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var,","and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if","hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key):","return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def","as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in","if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \"\"\"","op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var","shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x:","is called when the decorator is used with arguments if target is None:","isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node","# pylint: disable=protected-access # Do not capture any internal stack traces fname =","or _noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\": context[self] = self.value =","self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) ==","other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other,","method. \"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes =","slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var =","positional arguments passed to the target kwargs : dict keywoard arguments passed to","The default hash returns an object id, whereas this function returns a hash","{self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name not in self.nodes.keys():","args): new_args = [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__","arguments passed to the target kwargs : dict keywoard arguments passed to the","Base class for nodes. Parameters ---------- args : tuple Positional arguments passed to","if interactive and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<')","hash returns an object id, whereas this function returns a hash of all","target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list,","def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s,","= f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def domain(self):","{self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and not","other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other,","graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`,","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph)","slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def","methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg","of the node cannot be found in the associated graph. \"\"\" # TODO:","dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph,","= nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ =","self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return","dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return id(self)","(\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if","ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is","graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return","(1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def","__and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","if isinstance(node, Node) or not is_iterable(node): node = [node] for n in node:","graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return","if isinstance(key, Node): name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str): for k","context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal value for","ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None,","= tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change in","return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple):","for indexing into. idx : tuple Tuple of either integer values or index/index_op","self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node))","method. name : str or None Name of the node or `None` to","def args(self, args): new_args = [] for arg in args: if isinstance(arg, Node):","finalized\" to work self._shape = UNSET_SHAPE else: shapes = [] for dim in","def shape(self): \"\"\" tuple : Shape of the output for a node. This","!= self): raise RuntimeError(f\"node '{node}' does not belong to {self} graph, instead belongs","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other,","sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape =","be found in the associated graph. \"\"\" # TODO: Need a way to","indices which are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\"","self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self","in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target","to evaluate. context : dict or None Context in which to evaluate the","axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape):","the new name with a unique stringwhich corresponds to the variable, index combination.","def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv,","messages = [] interactive = False if isinstance(node, Node) or not is_iterable(node): node",": dict[Node, object] Normalized context whose keys are node instances. Raises ------ ValueError",": Parent graph of this node. If graph is `None`, this is the","context information keyed by variable name. Returns ------- values : Node or tuple[object]","self._args = [] self._predeecessors = [] self._succesors = [] self.args = args if","op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x],",": Node or str Node instance or name of an node. Returns -------","and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node =","self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ ==","\"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other,","except Exception as ex: # pragma: no cover messages = [] interactive =","= _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if","slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain =","graph. \"\"\" name = name or uuid.uuid4().hex # TODO: Need a way to","self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and","function to evaluate the node. \"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls,","var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self,","other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self,","dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from","If `node` is not an `Node` instance or an node name. RuntimeError If","return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self,","> 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for","{indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res","shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\")","else f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else","context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node","slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and","or names to evaluate. context : dict or None Context in which to","self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name of the node and update","node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if","other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self)","__xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","callback=None): \"\"\" Evaluate the node given a context. Parameters ---------- context : dict","is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None):","__pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","(\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return","or None Context in which to evaluate the nodes. callback : callable or",": tuple positional arguments passed to the target kwargs : dict keywoard arguments","or name of an node. Returns ------- instantiated_node : Node Node instance. Raises","of positional arguments passed to `func`. kwargs : dict Mapping of keyword arguments","= tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape =","Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0:","__le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self,","\"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self,","shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x,","def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_,","graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this to check for finalzied","used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return","Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var}","name = self.var.name + \"[\" + \"][\".join(name) + \"]\" if name in self.graph.nodes:","graph. Parameters ---------- value : str Unique name of the node. Returns -------","= nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not =","This returns the functional hash of a particular node. The default hash returns","in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are greater than the variable","assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain)","in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args,","---------- dependencies : list Sequence of nodes to be evaluted before evaluating any","f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def domain(self): return","tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape])","in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property","self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph:","instantiated_node : Node Node instance. Raises ------ ValueError If `node` is not an","self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ):","and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d,","a name for the node and add the node to the graph self._name","return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def","#pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ =","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph)","not an `Node` instance or node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate","the node functionality. value : Any or None If a node has a","f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name and self._name","the decorator is used with arguments if target is None: return functools.partial(nodeop, **kwargs)","return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def","and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise","raise ValueError(f\"Invalid shape var for var index {self} with variable shape {self.var.shape}\") return","{list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name = [] if isinstance(key, Node):","Name of the node or `None` to use a random, unique identifier. shape","not None and name not in g.nodes: g = g.graph if name in","indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True","@property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray))","def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph)","Node or tuple[object] Output of the nodes given the context. Raises ------ ValueError","_flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\"","in this scope. \"\"\" # Add dependencies to the graph graph = Node.get_active_graph(graph)","var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index","slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def","\"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other,","import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for","== 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index {self}","__reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key,","self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self,","associated graph. \"\"\" name = name or uuid.uuid4().hex # TODO: Need a way","var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var,","a graph by replacing all node names with node instances. .. note:: This","Sequence of nodes to be evaluted before evaluating any nodes defined in this","self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx,","length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller","is not equal to the current ndoe as ewll if self.graph and name","else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = []","returning the explicitly given graph or using the default graph. Parameters ---------- graph","isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx","control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are executed before any nodes in","from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base","this node and discard the values. Parameters ---------- context : dict Normalised context","\"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other):","else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs):","nodes defined in this scope. \"\"\" # Add dependencies to the graph graph","order to enable \"is shape finalized\" to work self._shape = UNSET_SHAPE else: shapes","self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True)","**kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\" class var_index(Node): #","print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_","self._op_name = None self.op_name = op_name # Get the stack context so we","node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for","return self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var","for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] =","float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self,","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph)","self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get","= Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"]","f\"Eval Stack: {Node._eval_stack}\") if not single and not all([(idx_val - 1) >= indices[-1][idx]","else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None,","nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter)","or index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235","nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv)","var def set_name(self, name): \"\"\" Set the name for a variable index, making","Node wrapper for stateless functions. Parameters ---------- target : callable function to evaluate","single = True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches` must","all node names with node instances. .. note:: This function modifies the context","tuple positional arguments passed to the target kwargs : dict keywoard arguments passed","return ret def is_scalar(self, val=None): if val is not None and (not isinstance(val,","[] # TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph","pylint: disable=W0235 if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else","self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var","sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True","else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1])","and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self,","constant given a context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name)","reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack traces fname","other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other,","self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else","{dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently","of this node and discard the values. Parameters ---------- context : dict Normalised","Raises ------ ValueError If no `Graph` instance can be obtained. \"\"\" graph =","node. Raises ------ ValueError If an node with `value` already exists in the","the node to the graph self._name = None self.name = name or uuid.uuid4().hex","other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self)","key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name +","else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or","nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem)","isinstance(context, Mapping): raise ValueError(\"`context` must be a mapping.\") nodes = list(context) # Add","len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i)","graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def","or None Callback to be evaluated when an node is evaluated. kwargs :","other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self)","matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne","or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx,","= [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names)))","self._shape @property def var(self): return self @property def name(self): \"\"\"str : Unique name","else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","\"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain","name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph)","graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s =","nodes given a dictionary of node names with their values. .. note:: This","= var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def","and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var","other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret =","context=None, **kwargs): \"\"\" Inheriting nodes should implement this function to evaluate the node.","other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self)","[] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a","= nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ =","value must be placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \")","\"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other,","len(self) for i in range(num): yield self[i] def __eq__(self, other): return hash(self) ==","graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not","slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def","= nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat =","isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) ==","dict keywoard arguments passed to the target \"\"\" def __init__(self, target, *args, **kwargs):","keyword arguments `kwargs`. Parameters ---------- func : callable Function to call when the","@property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph","nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\"","shape {self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices,","\"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count > 0 else","ret def is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray)","idx : tuple Tuple of either integer values or index/index_op nodes. \"\"\" def","import contextlib import traceback import uuid import numpy as np import importlib from","= list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys()))","other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self)","and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key)","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other):","node. ValueError If `context` is not a mapping. \"\"\" if context is None:","other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): #","self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] =","slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except","polymath import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping,","graph = self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name,","f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name is not","fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property","nodes should implement this function to evaluate the node. \"\"\" return self(*args, context,","\"\"\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in","__rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and c.op_name in","= [] self._predeecessors = [] self._succesors = [] self.args = args if \"name\"","def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_,","*args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value =","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self,","of the nodes given the context. Raises ------ ValueError If `fetches` is not","before any nodes in this scope. Parameters ---------- dependencies : list Sequence of","nodes = list(context) # Add the keyword arguments for node in nodes: #","thereof.\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for","Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter","self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return","nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains)","self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return","---------- func : callable Function to call when the node is executed. args","graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape,","gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self,","graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return","can be a tuple of integers or parameter node names. graph : Node","`Node` instance, node name, or a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)):","set_name(self, name): \"\"\" Set the name of the node and update the graph.","return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def","(\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if","raise RuntimeError(f\"node '{node}' does not belong to {self} graph, instead belongs to\" f\"","shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif","graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return","other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other,","# Do not capture any internal stack traces fname = frame.filename if 'polymath'","len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i","\"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self,","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other):","Returns ------- values : Node or tuple[object] Output of the nodes given the","KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if","= nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ =","node\"\"\" return self._name @property def op_name(self): \"\"\" str : Operation name which describes","stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s` defined at:\\n\\n%s\" %","graph. Parameters ---------- graph : Node or None Graph to return or `None`","isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d)","delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add","be evaluated when an node is evaluated. Returns ------- value : object Output","var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not None","not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name]","get_active_graph(graph=None): \"\"\" Obtain the currently active graph instance by returning the explicitly given","\"\"\"str : Unique name of the node\"\"\" return self._name @property def op_name(self): \"\"\"","x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else",": dict Additional context information keyed by variable name. Returns ------- values :","other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","names. graph : Node or None Parent graph of this node. If graph","graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return","to evaluate the node. \"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node,","slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args,","\"\"\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`, this","graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","= nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ =","node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node in context:","not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret","self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or","None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)):","= var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape ==","op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0],","f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \"\"\" Obtain the currently active","= name or uuid.uuid4().hex # TODO: Need a way to check if the","enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_","instance, node name, or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node","shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name,","other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def","(\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if","other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","\\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for nodes. Parameters ---------- args","self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def","must be placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape","\"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other,","This node. Raises ------ ValueError If an node with `value` already exists in","if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx =","of the node given the context. \"\"\" # Evaluate all explicit dependencies first","c in fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph)","__ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" %","__add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other,","chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_","dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else:","mapping. \"\"\" if context is None: context = {} elif not isinstance(context, Mapping):","tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\"","== 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) ==","tuple([]) # Get a list of all dependencies relevant to this node self.dependencies","Node] or str or Node One or more `Node` instances or names to","and cannot be sliced for {self.name}\") elif isinstance(var, list): var = np.asarray(var) if","describes the node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name:","= list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices]","no cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to","@property def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name","return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph)","node instances. Raises ------ ValueError If the context specifies more than one value","check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified","Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for","get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var","ValueError(f\"duplicate value for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\",","node names with their values. .. note:: This function modifies the context in",":code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches : list[str or","node cannot be found in the associated graph. \"\"\" name = name or","= nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ =","irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le","= Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint:","to underscore private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict()","slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([]))","elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder or integer","list or numpy array, and cannot be sliced for {self.name}\") elif isinstance(var, list):","kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange this to underscore private variable self.kwargs","in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise","break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message","var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,)","use for execution, it can be set using `value`. kwargs : dict Keyword","self._name @property def op_name(self): \"\"\" str : Operation name which describes the node","shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape","node. \"\"\" return node_hash(self) def find_node(self, name): g = self.graph while g is","(slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key,","else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self):","@property def var(self): var, index_list = self.args return var def set_name(self, name): \"\"\"","in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args =","else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def","isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda","key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple):","new name with a unique stringwhich corresponds to the variable, index combination. Parameters","= _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else:","arguments passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\"","any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_","if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key:","---------- value : str Unique name of the node. Returns ------- self :","other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self)","= nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ =","slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\")","= np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) >","= node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node in","if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name","other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self,","if not single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val","value : Any or None If a node has a default value to","other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self)","shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self):","with node instances. .. note:: This function modifies the context in place. Use","= tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph","graph.nodes[name] = self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized()","name for the node and add the node to the graph self._name =","values or index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs): if \"domain\" in","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return","\"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper","functions. \"\"\" # This is called when the decorator is used with arguments","not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\"","gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert","variable name. Returns ------- values : Node or tuple[object] Output of the nodes","(0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single =","node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c","__bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \"\"\" This returns the","either integer values or index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs): if","unequal value for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\",","graph. op_name : str Operation name which describes the node functionality. value :","__rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self,","other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if",":code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context : dict[Node or","for node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if","with type {type(var)} is not a list or numpy array, and cannot be","fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return","Any or None If a node has a default value to use for","------ ValueError If the context specifies more than one value for any node.","def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return","(list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key,","Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx","{self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices =","new_args = [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ ==","cell if interactive and not fname.startswith('<'): break # pragma: no cover interactive =","isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape,","return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph)","op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else:","num = len(self) for i in range(num): yield self[i] def __eq__(self, other): return","evaluate the nodes. callback : callable or None Callback to be evaluated when","object] Additional context information keyed by variable name. Returns ------- normalized_context : dict[Node,","_flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain","other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self)","name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph","node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate the node given a context.","\"\"\" Node representing multi-dimensional operations performed on a node. Parameters ---------- target :","graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return","self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield self[i] def","unique identifier. shape : tuple or None Shape of the output for a","var(self): var, index_list = self.args return var def set_name(self, name): \"\"\" Set the","\"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape,","= self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None","f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node","context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node or","or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self,","of the output for a node. This can be a tuple of integers","graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return","compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_","ValueError If `fetches` is not an `Node` instance, node name, or a sequence","elif hasattr(key, \"__len__\") and not isinstance(key, str): for k in key: if isinstance(k,","node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']])","or None Name of the node or `None` to use a random, unique","return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def","\"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args","f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name not in self.nodes.keys(): raise","else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","_noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args,","the name of the node and update the graph. Parameters ---------- value :","not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None,","str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph","node : Node or str Node instance or name of an node. Returns","(Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable","other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self,","**kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value,","this is the top-level graph. \"\"\" return self._graph def preds(self): return self._preds def","__rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other,","def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from functions. \"\"\" # This","= [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\":","nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord)","self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\"","c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if","of nodes to be evaluted before evaluating any nodes defined in this scope.","slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def","to be evaluted before evaluating any nodes defined in this scope. \"\"\" #","instance but does not belong to this graph. \"\"\" if isinstance(node, str): return","all dependencies relevant to this node self.dependencies = [] if dependencies is None","graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k","context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or more nodes given a dictionary","= nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ =","if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return","graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self,","**kwargs) self.target = target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if","func : callable Function to call when the node is executed. args :","a dictionary of node names with their values. .. note:: This function modifies","= nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ =","other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return","the context. \"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self","= {key: partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs}","key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None):","var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape,","slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var,","self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s","Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103","in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count","\"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not","are used for executing this node. \"\"\" return tuple(self._args) @property def argnames(self): return","elif not shape or len(shape) == 0: # TODO: Change in order to","`value` already exists in the associated graph. KeyError If the current name of","context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node,","def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return","@contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are executed before any","nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super)","def args(self): \"\"\" tuple : Positional arguments which are used for executing this","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other):","(slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx =","self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self,","= nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt =","isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self)","len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape} in","self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx",">= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which",": callable function to evaluate the node args : tuple positional arguments passed","graph or using the default graph. Parameters ---------- graph : Node or None","for execution, it can be set using `value`. kwargs : dict Keyword arguments","return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self) def","**kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args","graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return","len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple)","**kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \"<func_op","= nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ =","indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res =",": Unique name of the node\"\"\" return self._name @property def op_name(self): \"\"\" str",": str Operation name which describes the node functionality. value : Any or","other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other):","id, whereas this function returns a hash of all attributes and subgraphs of","repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_","self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return","`None` to use a random, unique identifier. shape : tuple or None Shape","index, making sure to replicate the new name with a unique stringwhich corresponds","is executed. args : list Sequence of positional arguments passed to `func`. kwargs","\"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not","k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name =","= nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ =","elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s)","instance, node name, or a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches","in the associated graph. \"\"\" # TODO: Need a way to check if","__enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self):","functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if","== 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n","keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret else: name = []","nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct)","__mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices","return True def __hash__(self): return id(self) def func_hash(self): \"\"\" This returns the functional","class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\" class var_index(Node): # pylint:","all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args,","def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","{self.name}\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape)","= slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if","to the target kwargs : dict keywoard arguments passed to the target \"\"\"","target : cal The multi-dimensional variable used for indexing into. idx : tuple","self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or","other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self):","= slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"):","with their values. .. note:: This function modifies the context in place. Use","return self @property def name(self): \"\"\"str : Unique name of the node\"\"\" return","Need a way to check if the existing node is not equal to","but does not belong to this graph. \"\"\" if isinstance(node, str): return self.nodes[node]","def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul,","value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None):","self.shape = shape or tuple([]) # Get a list of all dependencies relevant","\"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count > 0","all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name}","evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs):","EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223","self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape","tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret =","in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop',","float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_","= Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed",": tuple Tuple of either integer values or index/index_op nodes. \"\"\" def __init__(self,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other,","of node names with their values. .. note:: This function modifies the context","@property def domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args return","to call when the node is executed. args : list Sequence of positional","__rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","== np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else","kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: domain = Domain(idx)","{self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name not in self.nodes.keys(): raise KeyError(f\"{name}","cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape,","target is None: return functools.partial(nodeop, **kwargs) # This is called when the decorator","found in the associated graph. \"\"\" name = name or uuid.uuid4().hex # TODO:","**kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call `func` with","dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other,","__rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self,","the node. callback : callable or None Callback to be evaluated when an","for a in self.args] @property def shape(self): \"\"\" tuple : Shape of the","way to check if the existing node is not equal to the current","def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should implement this function to","None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name =","\"\"\" Node wrapper for stateless functions. Parameters ---------- target : callable function to","raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph","fname = frame.filename if 'polymath' in fname: continue # Stop tracing at the","= nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ =","Callback to be evaluated when an node is evaluated. \"\"\" for node in","a node. \"\"\" return node_hash(self) def find_node(self, name): g = self.graph while g","= tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else:","if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([])","{self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs):","to avoid the context being modified. Parameters ---------- fetches : list[str or Node]","returns the functional hash of a particular node. The default hash returns an","g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph","Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e,","node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError):","node. Parameters ---------- target : cal The multi-dimensional variable used for indexing into.","dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for","(not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape","an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values of","a mapping. \"\"\" if context is None: context = {} elif not isinstance(context,","slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var,","other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self,","name which describes the node functionality. value : Any or None If a","node is evaluated. kwargs : dict Additional context information keyed by variable name.","elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx,","in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if","self.nodes[name] if node in context: raise ValueError(f\"duplicate value for node '{node}'\") context[node] =","If `fetches` is not an `Node` instance, node name, or a sequence thereof.","return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def","node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context)","in args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg)","the node and update the graph. Parameters ---------- value : str Unique name","= [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single else","\"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not","self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other):","= nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ =","return \"<node '%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value","if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes.","check if the existing node is not equal to the current ndoe as","nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf)","@graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name]","cgraph.graph return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self","= [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose","isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var,","\"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not","tuple[object] Output of the nodes given the context. Raises ------ ValueError If `fetches`","nodes in this scope. Parameters ---------- dependencies : list Sequence of nodes to","graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return",": Node or None Parent graph of this node. If graph is `None`,","return id(self) def func_hash(self): \"\"\" This returns the functional hash of a particular","{self}') return self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield","dict[Node or str, object] Context whose keys are node instances or names. kwargs","return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k])","self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other):","used for executing this node. \"\"\" return tuple(self._args) @property def argnames(self): return [a.name","{self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if","[partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value) for key,","nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass)","------- normalized_context : dict[Node, object] Normalized context whose keys are node instances. Raises","__rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args,","1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\"Invalid shape var for var index {self} with","not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self,","interactive cell if interactive and not fname.startswith('<'): break # pragma: no cover interactive","of the node. Returns ------- self : Node This node. Raises ------ ValueError","slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def","key): return self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if","return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph)","value to use for execution, it can be set using `value`. kwargs :","list of all dependencies relevant to this node self.dependencies = [] if dependencies","pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node object associated with the node","nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes)","self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def","def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph()","functionality. value : Any or None If a node has a default value","float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node):","belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not an `Node` instance","Output of the nodes given the context. Raises ------ ValueError If `fetches` is","\"index\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other,","mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self,","def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv,","else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return","nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round)","== Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self, key, value):","ValueError(\"`fetches` must be an `Node` instance, node name, or a \" \"sequence thereof.\")","context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args,","be a mapping.\") nodes = list(context) # Add the keyword arguments for node","name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value","__rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2","complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_","= var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else:","{var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist()","or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self,","1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1)","= cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self):","the target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\"","node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint:","\"<node '%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def","dict Mapping of keyword arguments passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager","= self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom)","self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other):","key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\"","not single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in","= list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return","def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_,","`_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes","= name return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of","graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node): # pylint:","graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name]","this to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must","node and update the graph. Parameters ---------- value : str Unique name of","name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack","key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key],","slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for","other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self)","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return","ValueError If `context` is not a mapping. \"\"\" if context is None: context","return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def","elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name,","return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name)","vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_","in which to evaluate the nodes. callback : callable or None Callback to","for fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self): return","evaluate an node. \"\"\" class var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values","raise ValueError(f\"var_index {self.name} has indices which are greater than the variable shape:\\n\" f\"\\tArgs:","% (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless","self.args = args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO:","is the top-level graph. op_name : str Operation name which describes the node","slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def","this function to evaluate the node. \"\"\" return self(*args, context, **kwargs) @classmethod def","= nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ =","other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other):","[fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single else tuple(values)","= self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal value for node '{node}'\")","values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single","self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def","Evaluate the dependencies of this node and discard the values. Parameters ---------- context","graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return","If a node has a default value to use for execution, it can","= self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif","kwargs : dict Additional context information keyed by variable name. Returns ------- values","def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub,","the dependencies of this node and discard the values. Parameters ---------- context :","def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph","def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","TODO: Change in order to enable \"is shape finalized\" to work self._shape =","= nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ =","self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}' does not belong to {self}","return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph)","self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other):","in c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values =","TODO: Need a way to check if the existing node is not equal","self in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context,","return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def","current name of the node cannot be found in the associated graph. \"\"\"","if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)):","self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name =","*args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call `func`","**kwargs): \"\"\" Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters","def name(self): \"\"\"str : Unique name of the node\"\"\" return self._name @property def","= nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ =","and discard the values. Parameters ---------- context : dict Normalised context in which","= args if \"name\" in kwargs: kwargs.pop(\"name\") self.added_attrs = [] # TODO: CHange","the default graph. Parameters ---------- graph : Node or None Graph to return","an node is evaluated. kwargs : dict Additional context information keyed by variable","return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph)","node in context: raise ValueError(f\"duplicate value for node '{node}'\") context[node] = value if","self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def var(self):","arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function to","and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else:","\"\"\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield #","issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_","return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is","fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c in fetches","reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_","abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def","is None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op,","else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) ==","for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name not in","slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def","@name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for","return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def","name of the node cannot be found in the associated graph. \"\"\" name","True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var):","nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input)","the node or `None` to use a random, unique identifier. shape : tuple","to the variable, index combination. Parameters ---------- value : str Unique name of","= self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)):","= None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs += [\"domain\",","return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other,","(\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if","self.shape == UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral):","= True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches` must be","_is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index))","else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and","else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n","True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\"`fetches` must be an","add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self,","Node) else a for a in self.args] @property def shape(self): \"\"\" tuple :","res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape","= functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node)","{node.graph}\") return node raise ValueError(f\"'{node}' is not an `Node` instance or node name\")","None If a node has a default value to use for execution, it","arguments passed to the `_evaluate` method. name : str or None Name of","\"\"\" Ensure that all `dependencies` are executed before any nodes in this scope.","1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value =","be a tuple of integers or parameter node names. \"\"\" return self._shape @property","xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self,","kwargs : dict[str, object] Additional context information keyed by variable name. Returns -------","context : dict Normalised context in which to evaluate the node. callback :","str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with","value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args =","If the context specifies more than one value for any node. ValueError If","'step']]) return node except Exception as ex: # pragma: no cover messages =","\"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not","to be evaluated when an node is evaluated. Returns ------- value : object","graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del","= nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor =","instance can be obtained. \"\"\" graph = graph or Node._graph_stack[-1] return graph def","by replacing all node names with node instances. .. note:: This function modifies","other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","instead belongs to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not an `Node`","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph)","nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat)","raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return","instances. Raises ------ ValueError If the context specifies more than one value for","Exception as ex: # pragma: no cover messages = [] interactive = False","dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add","__rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else:","return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for element","can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self):","args(self, args): new_args = [] for arg in args: if isinstance(arg, Node): if","stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None,","__rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self,","def __init__(self, target, *args, **kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if","op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies,","is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape)","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other,","DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices =","zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_","is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\"`context` must be","ex: # pragma: no cover messages = [] interactive = False if isinstance(node,","staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_","= Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target","self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\"","which describes the node functionality. value : Any or None If a node","self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else:","Change in order to enable \"is shape finalized\" to work self._shape = UNSET_SHAPE","------ ValueError If no `Graph` instance can be obtained. \"\"\" graph = graph","nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id)","functional hash of a particular node. The default hash returns an object id,","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other):","type {type(var)} is not a list or numpy array, and cannot be sliced","_wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call `func` with positional arguments `args`","self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other,","[a.name if isinstance(a, Node) else a for a in self.args] @property def shape(self):","other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","Tuple of either integer values or index/index_op nodes. \"\"\" def __init__(self, var, idx,","context: if c in fetches and c.op_name in [\"output\", \"state\", \"temp\"]: write_name =","if name not in self.nodes.keys(): raise KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys:","using `value`. kwargs : dict Keyword arguments passed to the `_evaluate` method. \"\"\"","= f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\"domain\"] def","default value to use for execution, it can be set using `value`. kwargs","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other):","raise ValueError(f\"'{node}' is not an `Node` instance or node name\") def instantiate_graph(self, context,","defined in this scope. \"\"\" # Add dependencies to the graph graph =","# This is called when the decorator is used without arguments @functools.wraps(target) def","functools from numbers import Integral, Rational, Real import contextlib import traceback import uuid","def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_,","try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs)","node. \"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\"","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph)","self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx =","node name. Parameters ---------- node : Node or str Node instance or name","self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node):","self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an node. \"\"\" class var_index(Node):","to use for execution, it can be set using `value`. kwargs : dict","fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack)))","__getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self","= nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ =","nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list)","self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph != self):","the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph :","self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self,","object] Context whose keys are node instances or names. kwargs : dict[str, object]","hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__","explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i","Obtain the currently active graph instance by returning the explicitly given graph or","node to the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return add(other, self,","node is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context,","currently active graph instance by returning the explicitly given graph or using the","to evaluate node `%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from","return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph)","execution, it can be set using `value`. kwargs : dict Keyword arguments passed","**kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs):","which to evaluate the node. callback : callable or None Callback to be","np import importlib from .graph import Graph from .domain import Domain from .util","isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([]))","ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter","**kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs)","# pylint: disable=C0103,W0223 \"\"\" Node representing values of a variable corresponding to input","\"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node","Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder","------- instantiated_node : Node Node instance. Raises ------ ValueError If `node` is not","and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New:","name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n =","return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__or__(self) def","indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape,","nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open)","given a dictionary of node names with their values. .. note:: This function","else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context, callback=None):","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other):","var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret","filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_","= deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self,","{self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name and","be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self)","new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self,","var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var,","__rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape","with the node name. Parameters ---------- node : Node or str Node instance","nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt)","Evaluate the node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__","inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def","set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral)","Parent graph of this node. If graph is `None`, this is the top-level","**kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"],","setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor","[partial(arg) for arg in self.args] kwargs = {key: partial(value) for key, value in","**kwargs): \"\"\" Inheriting nodes should implement this function to evaluate the node. \"\"\"","else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__le__(self)","def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) ==","Evaluate one or more nodes given a dictionary of node names with their","the current ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate","other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self)","def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)): return getitem(self,","if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx","**kwargs) # This is called when the decorator is used without arguments @functools.wraps(target)","if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx","= slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self,","other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","arguments passed to the target \"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] =","evaluted before evaluating any nodes defined in this scope. \"\"\" # Add dependencies","if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var","self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for","graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key],","called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return","nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context:","graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index","else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain =","return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape =","slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def","var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \"index\"): slice1_idx = slice1_var.domain","self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other):","op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name","to this node self.dependencies = [] if dependencies is None else dependencies if","== 0: # TODO: Change in order to enable \"is shape finalized\" to","self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def","scope. \"\"\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield","nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr)","or parameter node names. graph : Node or None Parent graph of this","graph self._name = None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name","if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid","elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0],","return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not","**kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args,","all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_","None self.op_name = op_name # Get the stack context so we can report","Parameters ---------- context : dict Normalised context in which to evaluate the node.","find {name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this to","the currently active graph instance by returning the explicitly given graph or using","graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return","belong to this graph. \"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node):","\"op_name\" in kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\"))","decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner,","len(shape) == 0: # TODO: Change in order to enable \"is shape finalized\"","\"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop()","to avoid the context being modified. Parameters ---------- context : dict[Node or str,","super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def","evaluated when an node is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback)","self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting","= [] # TODO: CHange this to underscore private variable self.kwargs = kwargs","name return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of this","isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else:","arguments if target is None: return functools.partial(nodeop, **kwargs) # This is called when","isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self)","value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape:","self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"] = f\"{fnc.__module__}.{fnc.__name__}\" def __getitem__(self, key): return self @property def","or None Callback to be evaluated when an node is evaluated. \"\"\" for","value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback","= nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos =","self self._name = name return self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the","def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain:","instances or names to evaluate. context : dict or None Context in which","isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self,","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self,","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ne__(self) def __gt__(self, other): return gt(self,","idx, idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are greater than","a mapping.\") nodes = list(context) # Add the keyword arguments for node in","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return","or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs} \") self._shape = tuple(shapes) @staticmethod","instance or node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph by","and subgraphs of a node. \"\"\" return node_hash(self) def find_node(self, name): g =","items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating","Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return","__ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self,","\"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other,","op_name : str Operation name which describes the node functionality. value : Any","Ensure that all `dependencies` are executed before any nodes in this scope. Parameters","elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else:","not in self.added_attrs} # Evaluate the node callback = callback or _noop_callback with","self.nodes = Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors","Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance,","(node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class","context. Raises ------ ValueError If `fetches` is not an `Node` instance, node name,","one value for any node. ValueError If `context` is not a mapping. \"\"\"","s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in","slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add,","for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal","\"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other,","add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains","the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name = None","value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node]","other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self)","= nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index =","context[node] = value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not","\"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other,","other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self)","tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not None and","integer values or index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs): #","Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target = target self.added_attrs","an node is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self,","def __hash__(self): return id(self) def func_hash(self): \"\"\" This returns the functional hash of","Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple :","from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph)","return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph)","graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return","= nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ =","variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\" f\"\\tIndex Upper bounds:","graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\"","the associated graph. \"\"\" name = name or uuid.uuid4().hex # TODO: Need a","if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence):","it can be set using `value`. kwargs : dict Keyword arguments passed to","def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape","to evaluate the nodes. callback : callable or None Callback to be evaluated","graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if","is not a list or numpy array, and cannot be sliced for {self.name}\")","associated with the node name. Parameters ---------- node : Node or str Node","slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_",".domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class","def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg","op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name =","tuple Tuple of either integer values or index/index_op nodes. \"\"\" def __init__(self, var,","a list of all dependencies relevant to this node self.dependencies = [] if","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other):","if op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\": self._op_name = self.name else:","(\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if","the node args : tuple positional arguments passed to the target kwargs :","self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self,","with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable","is evaluated. kwargs : dict Additional context information keyed by variable name. Returns","in which to evaluate the node. callback : callable or None Callback to","else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self,","= graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate","op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return","2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain =","TypeError(f\"Shape value must be placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\" f\"\\n\\t{self.kwargs}","= self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([])","self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is","= self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] =","self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var,","(\"slice_op\", \"var_index\", \"index\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if","name.append(key.name) elif hasattr(key, \"__len__\") and not isinstance(key, str): for k in key: if","Normalised context in which to evaluate the node. callback : callable or None","self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other):","domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list = self.args return var def","def call(func, *args, **kwargs): \"\"\" Call `func` with positional arguments `args` and keyword","Node or None Graph to return or `None` to use the default graph.",": Shape of the output for a node. This can be a tuple","self._shape = UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim,","dict[str, object] Additional context information keyed by variable name. Returns ------- normalized_context :","to check if the existing node is not equal to the current ndoe","dict or None Context in which to evaluate the nodes. callback : callable","\"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not","var_index(Node): # pylint: disable=C0103,W0223 \"\"\" Node representing values of a variable corresponding to","sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_","g.nodes[name] raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO:","and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if","isinstance(node, Node): if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\"node","= value if node.op_name in [\"placeholder\", \"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized():","other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self,","= g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name} in","def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args))","= nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ =","in self.added_attrs} # Evaluate the node callback = callback or _noop_callback with callback(self,","one or more nodes given a dictionary of node names with their values.","other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other):","ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_","return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph)","nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash)","else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret","Additional context information keyed by variable name. Returns ------- normalized_context : dict[Node, object]","= nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior =","name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property def var(self): var, index_list","idx_val in enumerate(var.shape)]): raise ValueError(f\"var_index {self.name} has indices which are greater than the","wrapper for stateless functions. Parameters ---------- target : callable function to evaluate the","(\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if","{var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\"","Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \"[\" + \"][\".join(name)","__ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","+ \"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self,","(\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if","(\"slice_op\", \"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if","used for indexing into. idx : tuple Tuple of either integer values or","= Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else:","nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from functions. \"\"\" # This is","= nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ =","nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted)","\"index\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other,","functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key:","all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx)","isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice):","if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return","nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul)","in node) if isinstance(node, list): return [partial(element) for element in node] if isinstance(node,","self.target = target @property def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key,","in kwargs \\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if","DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import","`None`, this is the top-level graph. \"\"\" return self._graph def preds(self): return self._preds","= np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self,","so we can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property","= tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape)","in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res = res[0]","s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape)","else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other,","def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\"domain\"] def get_index_nodes(self, slice1_var=None,","\\ else f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list)","nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex)","shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif","self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this","isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx,","def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return","was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph","nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple)","domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target =","le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul","return [a.name if isinstance(a, Node) else a for a in self.args] @property def","of all dependencies relevant to this node self.dependencies = [] if dependencies is","callable or None Callback to be evaluated when an node is evaluated. kwargs","{partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr))","__sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\"","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__radd__(self) def __radd__(self, other): return","of integers or parameter node names. graph : Node or None Parent graph","to the current ndoe as ewll if self.graph and name in self.graph.nodes: raise","\"var_index\", \"index\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not","other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self,","which are greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode","(\"slice_op\", \"var_index\", \"index\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if","= nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add =","\"\"\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies`","callable or None Callback to be evaluated when an node is evaluated. \"\"\"","nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt)","graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return","other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other,","graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return","or Node One or more `Node` instances or names to evaluate. context :","var index {self} with variable shape {self.var.shape}\") return True else: return self.var.shape ==","(np.ndarray, list)): raise TypeError(f\"Variable {var} with type {type(var)} is not a list or","other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other,","import operator from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers","else kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx","def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","**kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other,","modified. Parameters ---------- fetches : list[str or Node] or str or Node One","for executing this node. \"\"\" return tuple(self._args) @property def argnames(self): return [a.name if","context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs):","else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","list Sequence of nodes to be evaluted before evaluating any nodes defined in","nodes to be evaluted before evaluating any nodes defined in this scope. \"\"\"","node instances or names. kwargs : dict[str, object] Additional context information keyed by","other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self)","var, index_list = self.args return var def set_name(self, name): \"\"\" Set the name","indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self,","node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node","in context: raise ValueError(f\"duplicate value for node '{node}'\") context[node] = value if node.op_name","given the context. \"\"\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if","def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set","\"state\", \"input\", \"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def",": callable Function to call when the node is executed. args : list","= self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter","op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def","return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph)","interactive and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame)","`func`. kwargs : dict Mapping of keyword arguments passed to `func`. \"\"\" return","or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and","nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr)","has a default value to use for execution, it can be set using","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other):","def var(self): return self @property def name(self): \"\"\"str : Unique name of the","other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self)","res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self,","kwargs.pop(\"domain\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx =","tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var,","cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return","of a particular node. The default hash returns an object id, whereas this","add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in","*args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\ else f\"{target.__name__}\" if","__pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key):","is_iterable(node): node = [node] for n in node: stack = [] if isinstance(n,","other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self,","in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\"duplicate value for","f\"{target.__name__}\" if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\")","__rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self,","obtained. \"\"\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): #","combination. Parameters ---------- value : str Unique name of the node. Returns -------","def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name,","UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)):","if slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args if","as ex: # pragma: no cover messages = [] interactive = False if","str Operation name which describes the node functionality. value : Any or None","isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def","other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self)","raise ValueError(f\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\" f\"dimensions for","other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self,","for name, value in kwargs.items(): node = self.nodes[name] if node in context: raise","names. kwargs : dict[str, object] Additional context information keyed by variable name. Returns","(node.graph != self): raise RuntimeError(f\"node '{node}' does not belong to {self} graph, instead","self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rtruediv__(self) def __floordiv__(self, other):","in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context","_wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args,","False else: raise ValueError(\"`fetches` must be an `Node` instance, node name, or a","arguments `kwargs`. Parameters ---------- func : callable Function to call when the node","other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not an `Node` instance or node","= nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains =","else: graph.nodes[name] = self self._name = name return self def __getitem__(self, key): if","return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \"\"\" Ensure that all `dependencies` are","KeyError(f\"{name} not in {self.name} keys:\\n\" f\"Node keys: {list(self.nodes.keys())}\") ret = self.nodes[name] return ret","isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not","np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1:","\"\"\" name = name or uuid.uuid4().hex # TODO: Need a way to check","Returns ------- self : Node This node. Raises ------ ValueError If an node","== 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)):","var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\"Variable {var} with type","or tuple[object] Output of the nodes given the context. Raises ------ ValueError If","_noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class for nodes.","with a unique stringwhich corresponds to the variable, index combination. Parameters ---------- value","if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not","shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder or","`Graph` instance can be obtained. \"\"\" graph = graph or Node._graph_stack[-1] return graph","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rmod__(self) def __rmod__(self, other):","self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if key not","= nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ =","nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any)","\"var_index\", \"index\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not","\"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs):","len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1])","being modified. Parameters ---------- context : dict[Node or str, object] Context whose keys","def func_hash(self): \"\"\" This returns the functional hash of a particular node. The","elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return","self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node","sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self,","**kwargs) return n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self):","which are used for executing this node. \"\"\" return tuple(self._args) @property def argnames(self):","context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs)","\"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional operations","elif self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def","aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self,","self, other, graph=self.graph) def __repr__(self): return \"<slice_%s '%s'>\" % (self.target.__name__, self.name) class func_op(Node):","self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self,","args = [partial(arg) for arg in self.args] kwargs = {key: partial(value) for key,","slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other):","matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other,","property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_","kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def","Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real import contextlib","self.__class__.__name__ == \"Node\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self,","an `Node` instance or node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a","if the existing node is not equal to the current ndoe as ewll","return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph)","func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \"\"\" Call","value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return id(self) def","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__xor__(self) def __lt__(self, other): return","shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain)","eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt","of the node or `None` to use a random, unique identifier. shape :","kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def","= nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg =","to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be","if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__add__(self) def __sub__(self, other): return sub(self,","> 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape):","(Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be","run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or more nodes given","self.__class__.__name__ == \"Node\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] =","= nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ =","self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\"domain\"]","self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in args: if","else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","\"\"\" str : Operation name which describes the node functionality. \"\"\" return self._op_name","def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","len(self.shape): raise KeyError(f\"Invalid key shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name =","(slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return","itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt","Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to","node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs)","= nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ =","return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif","__truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other,","self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if","pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self,","if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx]","multi-dimensional operations performed on a node. Parameters ---------- target : cal The multi-dimensional","= self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain elif","graph. \"\"\" return self._graph def preds(self): return self._preds def succs(self): return self._preds def","other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self,","{self.domain}\\n\" f\"Eval Stack: {Node._eval_stack}\") if not single and not all([(idx_val - 1) >=","variable, index combination. Parameters ---------- value : str Unique name of the node.","UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral): return False","def evaluate(self, context, callback=None): \"\"\" Evaluate the node given a context. Parameters ----------","for var index {self} with variable shape {self.var.shape}\") return True else: return self.var.shape","in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches","nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv)","of this node. If graph is `None`, this is the top-level graph. \"\"\"","\"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] =","return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def","__gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","evaluate the node. callback : callable or None Callback to be evaluated when","return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph)","Instantiate a graph by replacing all node names with node instances. .. note::","{key: partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs} #","callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_","def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul,","ValueError If no `Graph` instance can be obtained. \"\"\" graph = graph or","value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx =","context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args","associated graph. \"\"\" # TODO: Need a way to check if the existing","len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not match","an node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If","self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\" Set the name of","or a sequence thereof. \"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches] single","nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth)","or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else:","nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next)","target kwargs : dict keywoard arguments passed to the target \"\"\" def __init__(self,","for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim))","fetches = [fetches] single = True elif isinstance(fetches, Sequence): single = False else:","nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex)","`context` is not a mapping. \"\"\" if context is None: context = {}","pylint: disable=C0103,R0903 \"\"\" Node wrapper for stateless functions. Parameters ---------- target : callable","# pragma: no cover messages = [] interactive = False if isinstance(node, Node)","this to underscore private variable self.kwargs = kwargs self.graph = graph self._shape =","evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node or constant given a context.","key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key])","parameter node names. \"\"\" return self._shape @property def var(self): return self @property def","return node_hash(self) def find_node(self, name): g = self.graph while g is not None","`Node` instances or names to evaluate. context : dict or None Context in","def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","does not belong to this graph. \"\"\" if isinstance(node, str): return self.nodes[node] if","\"\"\" def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs","= [] self._args = [] self._predeecessors = [] self._succesors = [] self.args =","list): return [partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value)","var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph)","{self.args}\") if self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name,","when an node is evaluated. \"\"\" for node in self.dependencies: node.evaluate(context, callback) def","\"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0","self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other):","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other,","return \"/\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self ==","name): \"\"\" Set the name for a variable index, making sure to replicate","elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif","enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float):","else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and","other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self,","context so we can report where the node was defined self._stack = traceback.extract_stack(limit=1)","if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") elif","self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other):","an `Node` instance, node name, or a sequence thereof. \"\"\" if isinstance(fetches, (str,","\"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self,","# Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs)","else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op,","name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in args:","graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return","def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name =","self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\"","RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update this","(slice_op, var_index)) or _is_node_type_instance(slice1_var, \"GroupNode\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if","output for a node. This can be a tuple of integers or parameter","if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name,","tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \"\"\"","return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node representing multi-dimensional","This function modifies the context in place. Use :code:`context=context.copy()` to avoid the context","or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by","node or constant given a context. \"\"\" Node.evaluated_nodes += 1 try: if isinstance(node,","slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def","def domain(self): return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and","greater than the variable shape:\\n\" f\"\\tArgs: {self.args}\\n\" f\"\\tVar shape: {var.shape}\\n\" f\"\\tNode shape: {self.var.shape}\\n\"","return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph)","\"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other,","values or index/index_op nodes. \"\"\" def __init__(self, var, idx, name=None, **kwargs): # pylint:","other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self,","bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_","shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies","slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args)","== 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if","# Stop tracing at the last interactive cell if interactive and not fname.startswith('<'):","isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and","name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \"Node\": if isinstance(key, (slice, Integral)):","numbers import Integral, Rational, Real import contextlib import traceback import uuid import numpy","context being modified. Parameters ---------- fetches : list[str or Node] or str or","and c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")])","isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node,","the node and add the node to the graph self._name = None self.name","return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \"<node","a for a in self.args] @property def shape(self): \"\"\" tuple : Shape of","return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a for","n in node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack):","in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var =","arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else","node) if isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict):","\"index\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other,","nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii)",": str or None Name of the node or `None` to use a","@property def domain(self): return Domain(tuple([])) @property def args(self): \"\"\" tuple : Positional arguments","c.op_name in [\"output\", \"state\", \"temp\"]: write_name = \"/\".join([f\"{i}{c.write_count-1}\" for i in c.name.split(\"/\")]) if","this node. If graph is `None`, this is the top-level graph. \"\"\" return","return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element)","the context in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters","{self.var.name} - {self.var.op_name}\" f\"dimensions for slice {self.args[0].name} with {out_shape}.\\n\" f\"Domain: {self.domain}\\n\" f\"Eval Stack:","node. This can be a tuple of integers or parameter node names. graph","indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs()","first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the parents","var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None,","node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs):","with arguments if target is None: return functools.partial(nodeop, **kwargs) # This is called","representing values of a variable corresponding to input index values. Parameters ---------- var","_evaluate(self, *args, context=None, **kwargs): \"\"\" Inheriting nodes should implement this function to evaluate","or `None` to use a random, unique identifier. shape : tuple or None","return self.kwargs[\"domain\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) ==","= nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ =","or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack context","node name. RuntimeError If `node` is an `Node` instance but does not belong","---------- graph : Node or None Graph to return or `None` to use","float): shapes.append(int(dim)) else: raise TypeError(f\"Shape value must be placeholder or integer value for","**kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else:","is `None`, this is the top-level graph. \"\"\" return self._graph def preds(self): return","__and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other,","if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx]))","other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other):","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self, other): return","stack.append(frame) stack = \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s` defined at:\\n\\n%s\"","else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs) self.target","slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain","x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2)","return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\"{fnc.__name__}\" self.kwargs[\"target\"]","target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property","__or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else",".util import _noop_callback, _flatten_iterable, node_hash, \\ _is_node_type_instance, is_iterable class Node(object): \"\"\" Base class","set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self):","tuple : Positional arguments which are used for executing this node. \"\"\" return","self._predeecessors = [] self._succesors = [] self.args = args if \"name\" in kwargs:","= nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller =","__call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \"\"\" Failed to evaluate an","add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ,","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other,","tuple Positional arguments passed to the `_evaluate` method. name : str or None","0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes =","args(self): \"\"\" tuple : Positional arguments which are used for executing this node.","partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate","return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert","or parameter node names. \"\"\" return self._shape @property def var(self): return self @property","value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None:","self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback = callback","Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key)","@property def argnames(self): return [a.name if isinstance(a, Node) else a for a in","tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for","not in self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}' does not belong","(tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape)","Output of the node given the context. \"\"\" # Evaluate all explicit dependencies","other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list",".graph import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash,","values. Parameters ---------- var : Node The multi-dimensional variable used for indexing into.","---------- target : cal The multi-dimensional variable used for indexing into. idx :","if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args =","*args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" % \\ (self.name,","= callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \"Node\": context[self] =","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph)","\"\"\" def __init__(self, target, *args, **kwargs): if \"domain\" in kwargs: domain = tuple(kwargs.pop(\"domain\"))","\"var_index\", \"index\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not","def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne,","__radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self,","node names. \"\"\" return self._shape @property def var(self): return self @property def name(self):","(\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if","len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var =","variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are","node name, or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node in","this is the top-level graph. op_name : str Operation name which describes the","other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self,","the current name of the node cannot be found in the associated graph.","tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx],","lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__gt__(self) def __le__(self,","argnames(self): return [a.name if isinstance(a, Node) else a for a in self.args] @property","return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mul__(self) def","other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self)","= nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ =","return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self,","isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\"Invalid key","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self,","yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ =","False for s in self.shape: if not isinstance(s, Integral): return False return True","def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return","Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var,","This is called when the decorator is used with arguments if target is","other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","to\" f\" {node.graph}\") return node raise ValueError(f\"'{node}' is not an `Node` instance or","not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] ==","def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property","nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals)","\"\"\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621","__invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self,","import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\","= list(context) # Add the keyword arguments for node in nodes: # pylint:disable=W0621","capture any internal stack traces fname = frame.filename if 'polymath' in fname: continue","internal stack traces fname = frame.filename if 'polymath' in fname: continue # Stop","graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return","def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod,","rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self,","is used with arguments if target is None: return functools.partial(nodeop, **kwargs) # This","If `node` is an `Node` instance but does not belong to this graph.","other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\",","name, or a \" \"sequence thereof.\") fetches = [self.instantiate_node(node) for node in fetches]","context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg)","np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i]","= self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node)","if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node,","def __repr__(self): return \"<node '%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key]","# Add the keyword arguments for node in nodes: # pylint:disable=W0621 value =","graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__and__(self) def __or__(self, other): return","return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__floordiv__(self) def","init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv","slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def","= nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ =","s in self.shape: if not isinstance(s, Integral): return False return True def set_shape(self,","= nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ =","callback=None): \"\"\" Evaluate the dependencies of this node and discard the values. Parameters","------ ValueError If `node` is not an `Node` instance or an node name.","node. The default hash returns an object id, whereas this function returns a","x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None):","other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self,","\"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs)","current ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name","is not an `Node` instance or node name\") def instantiate_graph(self, context, **kwargs): \"\"\"","@args.setter def args(self, args): new_args = [] for arg in args: if isinstance(arg,","representing multi-dimensional operations performed on a node. Parameters ---------- target : cal The",": Positional arguments which are used for executing this node. \"\"\" return tuple(self._args)","other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self,","pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv","index combination. Parameters ---------- value : str Unique name of the node. Returns","return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph)","shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape =","note:: This function modifies the context in place. Use :code:`context=context.copy()` to avoid the","node. \"\"\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else","class slice_op(Node): \"\"\" Node representing multi-dimensional operations performed on a node. Parameters ----------","in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot","*args, context=None, **kwargs): \"\"\" Inheriting nodes should implement this function to evaluate the","slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\"{target.__module__}.{target.__name__}\", domain=domain, **kwargs)","@functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def","Raises ------ ValueError If an node with `value` already exists in the associated","Stack: {Node._eval_stack}\") if not single and not all([(idx_val - 1) >= indices[-1][idx] for","not in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise","the graph. Parameters ---------- value : str Unique name of the node. Returns","[] interactive = False if isinstance(node, Node) or not is_iterable(node): node = [node]","for a variable index, making sure to replicate the new name with a","np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2,","and update the graph. Parameters ---------- value : str Unique name of the","var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single =","set using `value`. kwargs : dict Keyword arguments passed to the `_evaluate` method.","keys are node instances or names. kwargs : dict[str, object] Additional context information","else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","in node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack): #","Rational, Real import contextlib import traceback import uuid import numpy as np import","**kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list):","`node` is an `Node` instance but does not belong to this graph. \"\"\"","__matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph)","the node object associated with the node name. Parameters ---------- node : Node","name for a variable index, making sure to replicate the new name with","raise TypeError(f\"Shape value must be placeholder or integer value for {self.name}\\n\" f\"\\tDim: {dim}\"","**kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \"\"\" Evaluate an node or constant",": Node The multi-dimensional variable used for indexing into. idx : tuple Tuple","in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in","to use the default graph. Raises ------ ValueError If no `Graph` instance can","dict Additional context information keyed by variable name. Returns ------- values : Node","self def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of this node and","oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_","context specifies more than one value for any node. ValueError If `context` is","arguments passed to the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack = []","graph is `None`, this is the top-level graph. op_name : str Operation name","`Node` instance or node name\") def instantiate_graph(self, context, **kwargs): \"\"\" Instantiate a graph","Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single = False","rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self,","@op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \"Node\":","the variable, index combination. Parameters ---------- value : str Unique name of the","nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range)","return context def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate one or","If graph is `None`, this is the top-level graph. op_name : str Operation","kwargs: domain = tuple(kwargs.pop(\"domain\")) if isinstance(kwargs[\"domain\"], list) else kwargs.pop(\"domain\") else: all_args = _flatten_iterable(args)","format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_","if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key),","out_shape = (1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices)","this scope. Parameters ---------- dependencies : list Sequence of nodes to be evaluted","else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self):","= self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args =","{self.var.shape}\\n\" f\"\\tIndex Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray)","node cannot be found in the associated graph. \"\"\" # TODO: Need a","f\"{target.__module__}.{target.__name__}\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\"slice_{target.__name__}\", **kwargs) self.target = target @property def domain(self):","+= [\"domain\", \"target\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target","name of the node and update the graph. Parameters ---------- value : str","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other,","at the last interactive cell if interactive and not fname.startswith('<'): break # pragma:","other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other):","len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)):","operations performed on a node. Parameters ---------- target : cal The multi-dimensional variable","not an `Node` instance or an node name. RuntimeError If `node` is an","specifies more than one value for any node. ValueError If `context` is not","args: if isinstance(arg, Node): if self.__class__.__name__ == \"Node\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args","= nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ =","Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes =","for the node and add the node to the graph self._name = None","importlib from .graph import Graph from .domain import Domain from .util import _noop_callback,","exists in the associated graph. KeyError If the current name of the node","and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\\n\\t\" f\"Existing: {self.graph.nodes[name].args}\\n\\t\" f\"New:","\"\"\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \"\"\" Evaluate","slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def","[] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None,","init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape])","which describes the node functionality. \"\"\" return self._op_name @op_name.setter def op_name(self, op_name): if","partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in","defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of","subgraphs of a node. \"\"\" return node_hash(self) def find_node(self, name): g = self.graph","None: context = {} elif not isinstance(context, Mapping): raise ValueError(\"`context` must be a","to use a random, unique identifier. shape : tuple or None Shape of","def __init__(self, target, *args, **kwargs): kwargs[\"op_name\"] = kwargs[\"op_name\"] if \"op_name\" in kwargs \\","graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return","\"\"\" Base class for nodes. Parameters ---------- args : tuple Positional arguments passed","value for node '{node}'\") context[node] = value if node.op_name in [\"placeholder\", \"state\", \"input\",","key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node","return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__mod__(self) def","pylint: disable=C0103,W0223 \"\"\" Node representing values of a variable corresponding to input index","def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub,","or None Callback to be evaluated when an node is evaluated. Returns -------","= cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name,","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph)","\"\"\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches,","other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if","\"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not","One or more `Node` instances or names to evaluate. context : dict or","key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return","whose keys are node instances or names. kwargs : dict[str, object] Additional context","__exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \"<node '%s'>\" % self.name","graph : Node or None Graph to return or `None` to use the","in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c in","'{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\" f\"New: {self.args}\") if self.graph: graph = self.graph if","self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self,","graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self)","= np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list","Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context : dict[Node","def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\"))","shape for {self.name}:\\n\" f\"Shape: {self.shape}\\n\" f\"Key: {key}\") name = f\"{self.name}{key}\" if name not","slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([]))","self.graph and name in self.graph.nodes: raise ValueError(f\"duplicate name '{name}' in {self.graph.name}:\" f\"Existing: {self.graph.nodes[name].args}\\n\"","and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if","= nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ =","other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rrshift__(self) def __rrshift__(self, other):","of a variable corresponding to input index values. Parameters ---------- var : Node","\"Node\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)):","lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__lshift__(self) def __rshift__(self,","name, value in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\"duplicate","self.var.name + \"[\" + \"][\".join(name) + \"]\" if name in self.graph.nodes: return self.graph.nodes[name]","= Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors =","1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape)","nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne)","else a for a in self.args] @property def shape(self): \"\"\" tuple : Shape","True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape,","Node instance. Raises ------ ValueError If `node` is not an `Node` instance or","return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node,","node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\"node '{node}' does not","isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \"[\" +","insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def","return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph)","is an `Node` instance but does not belong to this graph. \"\"\" if","shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes","a hash of all attributes and subgraphs of a node. \"\"\" return node_hash(self)","`%s` defined at:\\n\\n%s\" % (n, stack) messages.append(message) raise ex from EvaluationError(\"\".join(messages)) @classmethod def","True def __hash__(self): return id(self) def func_hash(self): \"\"\" This returns the functional hash","if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \"[\"","last interactive cell if interactive and not fname.startswith('<'): break # pragma: no cover","'%s'>\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self):","ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret","isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not","values. .. note:: This function modifies the context in place. Use :code:`context=context.copy()` to","ret = self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name)","= nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow =","{self} with variable shape {self.var.shape}\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def","elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key])","uuid import numpy as np import importlib from .graph import Graph from .domain","\"var_index\", \"index\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self,","Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func","self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the parents partial","of a node. \"\"\" return node_hash(self) def find_node(self, name): g = self.graph while","\"\"\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in","disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min)","import Integral, Rational, Real import contextlib import traceback import uuid import numpy as","return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph)","return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph)","traceback.extract_stack(limit=1) @property def graph(self): \"\"\" polymath.srdfg.graph.Graph : Parent graph of this node. If","\"\"\" tuple : Shape of the output for a node. This can be","the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_","= nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem =","_is_node_type_instance(slice2_var, \"GroupNode\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \"index\"): slice2_idx = slice2_var.domain else: slice2_idx","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph)","graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return","callback=callback) for fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self):","Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\"domain\"] @property","= [] interactive = False if isinstance(node, Node) or not is_iterable(node): node =","= self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None","or index/index_op nodes. \"\"\" def __init__(self, target, *args, **kwargs): if \"domain\" in kwargs:","set_name(self, name): \"\"\" Set the name for a variable index, making sure to","def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key","\"var_index\", \"index\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not","e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([]))","le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__ge__(self) def __ne__(self,","def __repr__(self): return \"<var_index name=%s, index=%s>\" % (self.name, self.args) class slice_op(Node): \"\"\" Node","Function to call when the node is executed. args : list Sequence of","Mapping of keyword arguments passed to `func`. \"\"\" return func(*args, **kwargs) @contextlib.contextmanager def","using the default graph. Parameters ---------- graph : Node or None Graph to","g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\"Cannot find {name}","and (node.graph != self): raise RuntimeError(f\"node '{node}' does not belong to {self} graph,","raise ValueError(f\"duplicate unequal value for node '{node}'\") context[node] = value if node.op_name in","import uuid import numpy as np import importlib from .graph import Graph from","pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node):","is_iterable class Node(object): \"\"\" Base class for nodes. Parameters ---------- args : tuple","---------- node : Node or str Node instance or name of an node.","node = self.instantiate_node(node) if node in context: raise ValueError(f\"duplicate unequal value for node","other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def","isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape !=","kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\"duplicate value for node","locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_","= frame.filename if 'polymath' in fname: continue # Stop tracing at the last","= nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem =","{self.graph.nodes[name].args}\\n\\t\" f\"New: {self.args}\") if self.graph: graph = self.graph if self._name and self._name in","out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return","nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter)","for element in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value","= np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value =","context whose keys are node instances. Raises ------ ValueError If the context specifies","def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list)","for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res","else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\",","floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rfloordiv__(self) def __rfloordiv__(self,","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other,","Parameters ---------- target : callable function to evaluate the node args : tuple","pylint: disable=protected-access # Do not capture any internal stack traces fname = frame.filename","and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self,","0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n =","__rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else","def evaluate_dependencies(self, context, callback=None): \"\"\" Evaluate the dependencies of this node and discard","raise ValueError(\"`context` must be a mapping.\") nodes = list(context) # Add the keyword","list) else kwargs.pop(\"domain\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs)","not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other,","if self.graph: graph = self.graph if self._name is not None and self._name in","to the `_evaluate` method. \"\"\" _graph_stack = deque([None]) _eval_stack = [] stack_size =","{type(var)} is not a list or numpy array, and cannot be sliced for","nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval)","__init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \"domain\" in kwargs: domain","in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif","= [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape])","= \"\".join(traceback.format_list(reversed(stack))) message = \"Failed to evaluate node `%s` defined at:\\n\\n%s\" % (n,","while g is not None and name not in g.nodes: g = g.graph","array, and cannot be sliced for {self.name}\") elif isinstance(var, list): var = np.asarray(var)","slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other):","range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_","0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise","*args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args,","isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element)","variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape","__init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value","graph. KeyError If the current name of the node cannot be found in","the node. \"\"\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs):","_evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2)","call(self, *args, **kwargs) def __repr__(self): return \"<func_op '%s' target=%s args=<%d items>>\" % \\","+ \"[\" + \"][\".join(name) + \"]\" if name in self.graph.nodes: return self.graph.nodes[name] elif","= node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \"\"\" Evaluate","Parameters ---------- args : tuple Positional arguments passed to the `_evaluate` method. name","return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph !=","relevant to this node self.dependencies = [] if dependencies is None else dependencies","passed to `func`. kwargs : dict Mapping of keyword arguments passed to `func`.","\"\"\" if context is None: context = {} elif not isinstance(context, Mapping): raise","(len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs):","nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert)","_is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph)","def instantiate_node(self, node): # pylint:disable=W0621 \"\"\" Instantiate nodes by retrieving the node object","return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph)","Node representing values of a variable corresponding to input index values. Parameters ----------","the explicitly given graph or using the default graph. Parameters ---------- graph :","if not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if","nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor)","countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge","Upper bounds: {indices[-1]}\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x,","all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \"op_name\" in kwargs: kwargs.pop(\"op_name\") target_name = f\"{target.__module__}.{target.__name__}\" super(slice_op,","def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt,","executed before any nodes in this scope. Parameters ---------- dependencies : list Sequence","graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return","next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_","self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self,","\"output\", \"temp\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches,","other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other):","and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\"Index list does not match {var.shape} in {self.var.name}","s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val):","context: raise ValueError(f\"duplicate value for node '{node}'\") context[node] = value if node.op_name in","(self.name, self.kwargs[\"target\"], len(self.args)) def nodeop(target=None, **kwargs): \"\"\" Decorator for creating nodes from functions.","raise ex from EvaluationError(\"\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None,","be obtained. \"\"\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node):","returns a hash of all attributes and subgraphs of a node. \"\"\" return","graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return","`args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function to call","self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self):"],"string":"[\n \"raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update\",\n \"attr)) for attr in ['start', 'stop', 'step']]) return node except Exception as ex:\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other):\",\n \"= self.args return var def set_name(self, name): \\\"\\\"\\\" Set the name for a\",\n \"values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data)\",\n \"the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in\",\n \": dict or None Context in which to evaluate the nodes. callback :\",\n \"def set_name(self, name): \\\"\\\"\\\" Set the name of the node and update the\",\n \"__iter__(self): num = len(self) for i in range(num): yield self[i] def __eq__(self, other):\",\n \"self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key =\",\n \"= nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ =\",\n \"== \\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name):\",\n \"node. If graph is `None`, this is the top-level graph. \\\"\\\"\\\" return self._graph\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return\",\n \"> len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var,\",\n \"in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if key\",\n \"If an node with `value` already exists in the associated graph. KeyError If\",\n \"'stop', 'step']]) return node except Exception as ex: # pragma: no cover messages\",\n \"other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"performed on a node. Parameters ---------- target : cal The multi-dimensional variable used\",\n \"'%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for\",\n \"id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_\",\n \"1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context,\",\n \"return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node\",\n \"integers or parameter node names. graph : Node or None Parent graph of\",\n \"[] self._predeecessors = [] self._succesors = [] self.args = args if \\\"name\\\" in\",\n \"\\\"\\\"\\\" tuple : Positional arguments which are used for executing this node. \\\"\\\"\\\"\",\n \"\\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return\",\n \"list(context) # Add the keyword arguments for node in nodes: # pylint:disable=W0621 value\",\n \"information keyed by variable name. Returns ------- values : Node or tuple[object] Output\",\n \"values of a variable corresponding to input index values. Parameters ---------- var :\",\n \"{self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name is\",\n \"are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape:\",\n \"else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node)\",\n \"[] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do\",\n \"stringwhich corresponds to the variable, index combination. Parameters ---------- value : str Unique\",\n \"in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this to underscore private\",\n \"`Node` instance but does not belong to this graph. \\\"\\\"\\\" if isinstance(node, str):\",\n \"this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name\",\n \"name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are node\",\n \"in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name\",\n \"node except Exception as ex: # pragma: no cover messages = [] interactive\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self,\",\n \"#TODO: Update this to check for finalzied shape if self.shape == UNSET_SHAPE: raise\",\n \": Node This node. Raises ------ ValueError If an node with `value` already\",\n \"disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\")\",\n \"slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def\",\n \"for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape\",\n \"with `value` already exists in the associated graph. KeyError If the current name\",\n \"def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"or None Shape of the output for a node. This can be a\",\n \"is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs)\",\n \"tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return\",\n \"not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else:\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other,\",\n \"import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import functools\",\n \"= None self.op_name = op_name # Get the stack context so we can\",\n \"var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val,\",\n \"if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx =\",\n \"other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self,\",\n \"other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self,\",\n \"dictionary of node names with their values. .. note:: This function modifies the\",\n \"Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape\",\n \"mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self,\",\n \"domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional arguments which are\",\n \"tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x:\",\n \"@property def var(self): return self @property def name(self): \\\"\\\"\\\"str : Unique name of\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph)\",\n \"list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if\",\n \"name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node\",\n \"self): raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph, instead belongs to\\\"\",\n \"single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self,\",\n \"isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph)\",\n \"the stack context so we can report where the node was defined self._stack\",\n \"name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in\",\n \"update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key\",\n \"or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else:\",\n \"from numbers import Integral, Rational, Real import contextlib import traceback import uuid import\",\n \"def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def\",\n \"stack = [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access\",\n \"None and name not in g.nodes: g = g.graph if name in g.nodes:\",\n \"*args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node.\",\n \"DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for\",\n \"else: name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\"\",\n \"nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index)\",\n \"else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name):\",\n \"---------- args : tuple Positional arguments passed to the `_evaluate` method. name :\",\n \"`func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func :\",\n \"nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len)\",\n \"numpy as np import importlib from .graph import Graph from .domain import Domain\",\n \"values. Parameters ---------- context : dict Normalised context in which to evaluate the\",\n \"cover messages = [] interactive = False if isinstance(node, Node) or not is_iterable(node):\",\n \"other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other,\",\n \"np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1:\",\n \"nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift)\",\n \"nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_)\",\n \"def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"__len__(self): #TODO: Update this to check for finalzied shape if self.shape == UNSET_SHAPE:\",\n \"to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if\",\n \"slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def\",\n \"import functools from numbers import Integral, Rational, Real import contextlib import traceback import\",\n \"= False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var,\",\n \"found in the associated graph. \\\"\\\"\\\" # TODO: Need a way to check\",\n \"getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other):\",\n \"node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name =\",\n \"Shape of the output for a node. This can be a tuple of\",\n \"Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg\",\n \"if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name]\",\n \"arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name]\",\n \"nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile)\",\n \"context, callback=callback) for fetch in fetches] return values[0] if single else tuple(values) def\",\n \"and name not in g.nodes: g = g.graph if name in g.nodes: return\",\n \"np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape)\",\n \"self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral):\",\n \"other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other):\",\n \": list Sequence of positional arguments passed to `func`. kwargs : dict Mapping\",\n \"op_name # Get the stack context so we can report where the node\",\n \"nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq)\",\n \"new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k,\",\n \"Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x:\",\n \"node has a default value to use for execution, it can be set\",\n \"node. callback : callable or None Callback to be evaluated when an node\",\n \"cannot be found in the associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex\",\n \"return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def\",\n \"== 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value\",\n \"d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and\",\n \"keywoard arguments passed to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"]\",\n \"= nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ =\",\n \"shape finalized\\\" to work self._shape = UNSET_SHAPE else: shapes = [] for dim\",\n \"in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return\",\n \"[] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self,\",\n \"__rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self,\",\n \"nodes. callback : callable or None Callback to be evaluated when an node\",\n \"var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self,\",\n \"def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv,\",\n \"var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self,\",\n \"var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape)\",\n \"node raise ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\") def instantiate_graph(self,\",\n \"or not is_iterable(node): node = [node] for n in node: stack = []\",\n \"Node The multi-dimensional variable used for indexing into. idx : tuple Tuple of\",\n \"cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def\",\n \"index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if\",\n \"np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret\",\n \"__rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self,\",\n \"class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args : tuple Positional\",\n \"= True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if\",\n \"defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls,\",\n \"name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph\",\n \"other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self,\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return\",\n \"k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node,\",\n \"getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv\",\n \"% self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if\",\n \"the node is executed. args : list Sequence of positional arguments passed to\",\n \"s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape =\",\n \"== UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0]\",\n \"mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_\",\n \"return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def\",\n \"---------- context : dict[Node or str, object] Context whose keys are node instances\",\n \"fname: continue # Stop tracing at the last interactive cell if interactive and\",\n \"= self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape)\",\n \"domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\",\",\n \"__eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph)\",\n \"= [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str):\",\n \"name of the node cannot be found in the associated graph. \\\"\\\"\\\" #\",\n \"setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_\",\n \"Node One or more `Node` instances or names to evaluate. context : dict\",\n \"= [fetches] single = True elif isinstance(fetches, Sequence): single = False else: raise\",\n \"np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0]\",\n \"else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape\",\n \"f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val - 1)\",\n \"op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var\",\n \"passed to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"]\",\n \"tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_\",\n \"f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda\",\n \"or str or Node One or more `Node` instances or names to evaluate.\",\n \"self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\"\",\n \"to be evaluated when an node is evaluated. kwargs : dict Additional context\",\n \"__rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self,\",\n \"slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def\",\n \"return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def\",\n \"else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx])\",\n \"def op_name(self): \\\"\\\"\\\" str : Operation name which describes the node functionality. \\\"\\\"\\\"\",\n \"and keyword arguments `kwargs`. Parameters ---------- func : callable Function to call when\",\n \"*args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name def\",\n \"---------- fetches : list[str or Node] or str or Node One or more\",\n \"def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes\",\n \"------- values : Node or tuple[object] Output of the nodes given the context.\",\n \"if node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value\",\n \"= fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s`\",\n \"isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx)\",\n \"default graph. Parameters ---------- graph : Node or None Graph to return or\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self,\",\n \"super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self): return\",\n \"# Evaluate the node callback = callback or _noop_callback with callback(self, context): if\",\n \"slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def\",\n \"for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def\",\n \"that all `dependencies` are executed before any nodes in this scope. Parameters ----------\",\n \"the nodes given the context. Raises ------ ValueError If `fetches` is not an\",\n \"if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if\",\n \"op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name\",\n \"nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec)\",\n \"Inheriting nodes should implement this function to evaluate the node. \\\"\\\"\\\" return self(*args,\",\n \"or None If a node has a default value to use for execution,\",\n \"graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph =\",\n \"no cover messages = [] interactive = False if isinstance(node, Node) or not\",\n \"---------- target : callable function to evaluate the node args : tuple positional\",\n \"not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index {self} with variable shape\",\n \"callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a context. Parameters\",\n \"parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args]\",\n \"bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_\",\n \"a in self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape of the output\",\n \"keyed by variable name. Returns ------- values : Node or tuple[object] Output of\",\n \"self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"RuntimeError(f\\\"node '{node}' does not belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\")\",\n \"ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\",\",\n \"Parameters ---------- target : cal The multi-dimensional variable used for indexing into. idx\",\n \"__lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other,\",\n \"return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if\",\n \"for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater\",\n \"slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self,\",\n \"callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key: partial(value) for\",\n \"call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and keyword arguments\",\n \"return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name,\",\n \"data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of the node and\",\n \"making sure to replicate the new name with a unique stringwhich corresponds to\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if\",\n \"isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1\",\n \"def var(self): var, index_list = self.args return var def set_name(self, name): \\\"\\\"\\\" Set\",\n \"args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this\",\n \"= target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter def\",\n \"context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self,\",\n \"return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph)\",\n \"op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter\",\n \"(1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices =\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not\",\n \"name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if\",\n \"def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph)\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return\",\n \"class for nodes. Parameters ---------- args : tuple Positional arguments passed to the\",\n \"dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret =\",\n \"op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return\",\n \"val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2):\",\n \"finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for\",\n \"def target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def\",\n \"hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_\",\n \"integers or parameter node names. \\\"\\\"\\\" return self._shape @property def var(self): return self\",\n \"def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate\",\n \"def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index {self} with variable shape {self.var.shape}\\\")\",\n \"integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def\",\n \"self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif\",\n \"return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()):\",\n \"\\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a\",\n \"`None`, this is the top-level graph. op_name : str Operation name which describes\",\n \"nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller)\",\n \"len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape,\",\n \"else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx\",\n \"\\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx\",\n \"= traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node.\",\n \"enable \\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE else: shapes = []\",\n \"**kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"],\",\n \"other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self)\",\n \"if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return\",\n \"node with `value` already exists in the associated graph. KeyError If the current\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if\",\n \"self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args\",\n \"stack context so we can report where the node was defined self._stack =\",\n \"['start', 'stop', 'step']]) return node except Exception as ex: # pragma: no cover\",\n \"= tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1\",\n \"index values. Parameters ---------- var : Node The multi-dimensional variable used for indexing\",\n \"!= \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key,\",\n \"Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple):\",\n \"self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self,\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not\",\n \"the `_evaluate` method. name : str or None Name of the node or\",\n \"from polymath import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict,\",\n \"add the node to the graph self._name = None self.name = name or\",\n \"other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other,\",\n \"`kwargs`. Parameters ---------- func : callable Function to call when the node is\",\n \"creating nodes from functions. \\\"\\\"\\\" # This is called when the decorator is\",\n \"in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i\",\n \"and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *,\",\n \"other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"more than one value for any node. ValueError If `context` is not a\",\n \"object] Normalized context whose keys are node instances. Raises ------ ValueError If the\",\n \"= [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None,\",\n \"instances or names. kwargs : dict[str, object] Additional context information keyed by variable\",\n \"index {self} with variable shape {self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0]\",\n \"self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other):\",\n \"[] self._succesors = [] self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs\",\n \"other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"\\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex\",\n \"c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch,\",\n \"graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return\",\n \"= nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ =\",\n \"= self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and\",\n \"i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values\",\n \"tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a for a\",\n \"Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node\",\n \"Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph)\",\n \"len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key,\",\n \"return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is\",\n \"self.graph while g is not None and name not in g.nodes: g =\",\n \"other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self,\",\n \"belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}'\",\n \"slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target\",\n \"the associated graph. \\\"\\\"\\\" # TODO: Need a way to check if the\",\n \"and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf\",\n \"raise TypeError(f\\\"Variable {var} with type {type(var)} is not a list or numpy array,\",\n \"for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\",\",\n \"self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"]\",\n \"for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif\",\n \"modified. Parameters ---------- context : dict[Node or str, object] Context whose keys are\",\n \"**kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional\",\n \"self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self,\",\n \"f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name is not None and\",\n \"None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"]\",\n \"Do not capture any internal stack traces fname = frame.filename if 'polymath' in\",\n \"Parameters ---------- fetches : list[str or Node] or str or Node One or\",\n \"variable shape {self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var,\",\n \"np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape} in {self.var.name} -\",\n \"\\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or\",\n \"nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr)\",\n \"context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else:\",\n \"node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ----------\",\n \"in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to check for\",\n \"else: raise TypeError(f\\\"Shape value must be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim:\",\n \"slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except Exception as\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph)\",\n \"self.added_attrs} # Evaluate the node callback = callback or _noop_callback with callback(self, context):\",\n \"in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\")\",\n \"to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the\",\n \"**kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\")\",\n \"name : str or None Name of the node or `None` to use\",\n \"in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret =\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph)\",\n \"= nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ =\",\n \"value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False\",\n \"__rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph)\",\n \"not capture any internal stack traces fname = frame.filename if 'polymath' in fname:\",\n \"ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_\",\n \"frame.filename if 'polymath' in fname: continue # Stop tracing at the last interactive\",\n \"= nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ =\",\n \"op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name,\",\n \"op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value\",\n \"divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_\",\n \"ValueError If an node with `value` already exists in the associated graph. KeyError\",\n \"def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow,\",\n \"ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key),\",\n \"g = self.graph while g is not None and name not in g.nodes:\",\n \"single = False else: raise ValueError(\\\"`fetches` must be an `Node` instance, node name,\",\n \"@property def op_name(self): \\\"\\\"\\\" str : Operation name which describes the node functionality.\",\n \"for i in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other)\",\n \"nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_)\",\n \"normalized_context : dict[Node, object] Normalized context whose keys are node instances. Raises ------\",\n \"the context. Raises ------ ValueError If `fetches` is not an `Node` instance, node\",\n \"= [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in\",\n \"be evaluted before evaluating any nodes defined in this scope. \\\"\\\"\\\" # Add\",\n \"def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt,\",\n \"delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem\",\n \"__rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self,\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other):\",\n \"key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in\",\n \"node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a\",\n \"[] self._args = [] self._predeecessors = [] self._succesors = [] self.args = args\",\n \": Node or None Graph to return or `None` to use the default\",\n \"graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\"\",\n \"\\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph)\",\n \"else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise\",\n \"list Sequence of positional arguments passed to `func`. kwargs : dict Mapping of\",\n \"list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False):\",\n \"_is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var,\",\n \"slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain\",\n \"__gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self,\",\n \"op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if\",\n \"return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return\",\n \"sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_\",\n \"a list or numpy array, and cannot be sliced for {self.name}\\\") elif isinstance(var,\",\n \"= tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var,\",\n \"_is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx\",\n \"used with arguments if target is None: return functools.partial(nodeop, **kwargs) # This is\",\n \"without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper\",\n \"ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self)\",\n \"__rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return\",\n \"slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def\",\n \"attributes and subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g\",\n \"\\\"\\\"\\\" Instantiate nodes by retrieving the node object associated with the node name.\",\n \"This is called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args,\",\n \"else: raise ValueError(\\\"`fetches` must be an `Node` instance, node name, or a \\\"\",\n \"def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"of this node. If graph is `None`, this is the top-level graph. op_name\",\n \"arguments which are used for executing this node. \\\"\\\"\\\" return tuple(self._args) @property def\",\n \"return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key,\",\n \"multi-dimensional variable used for indexing into. idx : tuple Tuple of either integer\",\n \"indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var =\",\n \"reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if\",\n \"evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context,\",\n \"other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other):\",\n \"_evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return\",\n \"tuple or None Shape of the output for a node. This can be\",\n \"{var} with type {type(var)} is not a list or numpy array, and cannot\",\n \"hash of all attributes and subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def\",\n \"nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos)\",\n \"Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self)\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self, other,\",\n \"other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self,\",\n \"# pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object associated with the\",\n \"else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in\",\n \"i in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def\",\n \"than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex\",\n \"return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self):\",\n \"the node given the context. \\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context,\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other):\",\n \"self : Node This node. Raises ------ ValueError If an node with `value`\",\n \"__floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"stack traces fname = frame.filename if 'polymath' in fname: continue # Stop tracing\",\n \"self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return\",\n \"does not belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node\",\n \"of all attributes and subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self,\",\n \"str or Node One or more `Node` instances or names to evaluate. context\",\n \"implement this function to evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self,\",\n \"of the node and update the graph. Parameters ---------- value : str Unique\",\n \"if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self,\",\n \"if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}')\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self,\",\n \"self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self,\",\n \"__rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior)\",\n \"keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None):\",\n \"else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self):\",\n \"name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list))\",\n \"__repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] =\",\n \"init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names =\",\n \"context is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must\",\n \"else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self)\",\n \"= slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain,\",\n \"tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if\",\n \"= nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ =\",\n \"other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self)\",\n \"the context being modified. Parameters ---------- context : dict[Node or str, object] Context\",\n \"being modified. Parameters ---------- fetches : list[str or Node] or str or Node\",\n \"pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_\",\n \"nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_)\",\n \"other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self)\",\n \"self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None:\",\n \"name. RuntimeError If `node` is an `Node` instance but does not belong to\",\n \"= [] self._succesors = [] self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\")\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if\",\n \"(slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx =\",\n \"= nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ =\",\n \"if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if\",\n \"= nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ =\",\n \"str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_\",\n \"be evaluated when an node is evaluated. kwargs : dict Additional context information\",\n \"either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs):\",\n \"name which describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name):\",\n \"slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var,\",\n \"and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key,\",\n \"the values. Parameters ---------- context : dict Normalised context in which to evaluate\",\n \"# Get the stack context so we can report where the node was\",\n \"node instances. .. note:: This function modifies the context in place. Use :code:`context=context.copy()`\",\n \"aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self,\",\n \"self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph):\",\n \"return n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\"\",\n \"name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True\",\n \"Unique name of the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str :\",\n \"evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a context. Parameters ---------- context\",\n \"the top-level graph. op_name : str Operation name which describes the node functionality.\",\n \"has indices which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape:\",\n \"Node): if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}'\",\n \"Node) or not is_iterable(node): node = [node] for n in node: stack =\",\n \"self.args return var def set_name(self, name): \\\"\\\"\\\" Set the name for a variable\",\n \"nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map)\",\n \"is not a mapping. \\\"\\\"\\\" if context is None: context = {} elif\",\n \"name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph:\",\n \"Instantiate nodes by retrieving the node object associated with the node name. Parameters\",\n \"isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return\",\n \"else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return\",\n \"= [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access #\",\n \"a default value to use for execution, it can be set using `value`.\",\n \"np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) !=\",\n \"= (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single\",\n \"if self.shape == UNSET_SHAPE: return False for s in self.shape: if not isinstance(s,\",\n \"nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain =\",\n \"== 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int,\",\n \"name = name or uuid.uuid4().hex # TODO: Need a way to check if\",\n \"OrderedDict() self.shape = shape or tuple([]) # Get a list of all dependencies\",\n \"type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_\",\n \"isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape):\",\n \"other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"node name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches =\",\n \"name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\")\",\n \"all attributes and subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name):\",\n \"= nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ =\",\n \"\\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph\",\n \"RuntimeError If `node` is an `Node` instance but does not belong to this\",\n \"---------- var : Node The multi-dimensional variable used for indexing into. idx :\",\n \"other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self)\",\n \"= Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif\",\n \"isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_\",\n \"isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args)\",\n \"return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph)\",\n \"hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_\",\n \"Callback to be evaluated when an node is evaluated. kwargs : dict Additional\",\n \"not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem\",\n \"= self.graph while g is not None and name not in g.nodes: g\",\n \"\\\"\\\"\\\" return self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self,\",\n \"slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target =\",\n \"= False if isinstance(node, Node) or not is_iterable(node): node = [node] for n\",\n \"Additional context information keyed by variable name. Returns ------- values : Node or\",\n \"a node. This can be a tuple of integers or parameter node names.\",\n \"dependencies of this node and discard the values. Parameters ---------- context : dict\",\n \"\\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context:\",\n \"in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other):\",\n \"func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ---------- target\",\n \"res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)\",\n \"\\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key)\",\n \"for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single\",\n \"help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_\",\n \"self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op,\",\n \"return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph)\",\n \"np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None,\",\n \"else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list =\",\n \"open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_\",\n \"other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"tuple of integers or parameter node names. \\\"\\\"\\\" return self._shape @property def var(self):\",\n \"n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def\",\n \"def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other):\",\n \"if isinstance(node, Node): if node.name not in self.nodes and (node.graph != self): raise\",\n \"return self._shape @property def var(self): return self @property def name(self): \\\"\\\"\\\"str : Unique\",\n \"def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"__neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self,\",\n \"= nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv =\",\n \"(func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d))\",\n \"any nodes defined in this scope. \\\"\\\"\\\" # Add dependencies to the graph\",\n \"index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain\",\n \"Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches : list[str\",\n \"False if isinstance(node, Node) or not is_iterable(node): node = [node] for n in\",\n \"slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def\",\n \"if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and\",\n \"= nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem =\",\n \"= op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__\",\n \"= tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx,\",\n \"raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph\",\n \"nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf)\",\n \"Operation name which describes the node functionality. value : Any or None If\",\n \"= self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node]\",\n \"default graph. Raises ------ ValueError If no `Graph` instance can be obtained. \\\"\\\"\\\"\",\n \"is evaluated. Returns ------- value : object Output of the node given the\",\n \"return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None,\",\n \"Stop tracing at the last interactive cell if interactive and not fname.startswith('<'): break\",\n \"else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"or str Node instance or name of an node. Returns ------- instantiated_node :\",\n \"order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var,\",\n \"function to evaluate the node args : tuple positional arguments passed to the\",\n \"if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context,\",\n \"Integral, Rational, Real import contextlib import traceback import uuid import numpy as np\",\n \"explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate\",\n \"shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph,\",\n \"other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def\",\n \"other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self)\",\n \"enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater than the variable shape:\\\\n\\\"\",\n \"_graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def\",\n \"Set the name of the node and update the graph. Parameters ---------- value\",\n \"__add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret\",\n \"f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else\",\n \"**kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node names with node instances.\",\n \"isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key),\",\n \"node. If graph is `None`, this is the top-level graph. op_name : str\",\n \"not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not a\",\n \"f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name = [] if\",\n \"tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key]\",\n \"the node. Returns ------- self : Node This node. Raises ------ ValueError If\",\n \"{name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to check\",\n \"[] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for\",\n \"slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def\",\n \"{self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name is not None\",\n \"called when the decorator is used with arguments if target is None: return\",\n \"def find_node(self, name): g = self.graph while g is not None and name\",\n \"if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else: out_shape\",\n \"self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the node to\",\n \"name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\")\",\n \"executing this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a,\",\n \"None Callback to be evaluated when an node is evaluated. kwargs : dict\",\n \"__getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key,\",\n \"__floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other,\",\n \"passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size\",\n \"= nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ =\",\n \"nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge)\",\n \"variable index, making sure to replicate the new name with a unique stringwhich\",\n \"domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var,\",\n \"= self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name =\",\n \"self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = []\",\n \"else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property\",\n \"= nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ =\",\n \"node = self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\")\",\n \"in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack traces\",\n \"add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self,\",\n \"return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args =\",\n \"self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if\",\n \"= self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else:\",\n \"node names. graph : Node or None Parent graph of this node. If\",\n \"value : str Unique name of the node. Returns ------- self : Node\",\n \"a random, unique identifier. shape : tuple or None Shape of the output\",\n \"modifies the context in place. Use :code:`context=context.copy()` to avoid the context being modified.\",\n \"if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\"\",\n \"max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_\",\n \"else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x],\",\n \"5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None,\",\n \"def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def\",\n \"to be evaluated when an node is evaluated. \\\"\\\"\\\" for node in self.dependencies:\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return\",\n \"\\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None,\",\n \"is called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner):\",\n \"hash of a particular node. The default hash returns an object id, whereas\",\n \"list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args,\",\n \"name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return self._name @property def op_name(self):\",\n \"node object associated with the node name. Parameters ---------- node : Node or\",\n \"should implement this function to evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs)\",\n \"self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return\",\n \"variable used for indexing into. idx : tuple Tuple of either integer values\",\n \"\\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate\",\n \"placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not\",\n \"else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"instance by returning the explicitly given graph or using the default graph. Parameters\",\n \"isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if\",\n \"context in which to evaluate the node. callback : callable or None Callback\",\n \"is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self\",\n \"None Callback to be evaluated when an node is evaluated. Returns ------- value\",\n \"tuple : Shape of the output for a node. This can be a\",\n \"context = self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and\",\n \"kwargs : dict Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack =\",\n \"getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key,\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph)\",\n \"= nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge =\",\n \"Graph to return or `None` to use the default graph. Raises ------ ValueError\",\n \"if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs)\",\n \"self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self,\",\n \"evaluated. kwargs : dict Additional context information keyed by variable name. Returns -------\",\n \"ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self):\",\n \"other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self,\",\n \"kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target\",\n \"target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\"\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return\",\n \"Get a list of all dependencies relevant to this node self.dependencies = []\",\n \"getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice,\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return\",\n \"return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key,\",\n \"np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions\",\n \"target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self,\",\n \"functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name\",\n \"place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches :\",\n \"contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv\",\n \"while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self\",\n \"value for any node. ValueError If `context` is not a mapping. \\\"\\\"\\\" if\",\n \"shape(self): \\\"\\\"\\\" tuple : Shape of the output for a node. This can\",\n \"def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant given a\",\n \"if isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict): return\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if\",\n \"__lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other,\",\n \"other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other):\",\n \"# TODO: Change in order to enable \\\"is shape finalized\\\" to work self._shape\",\n \"discard the values. Parameters ---------- context : dict Normalised context in which to\",\n \"particular node. The default hash returns an object id, whereas this function returns\",\n \"\\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys\",\n \"when the node is executed. args : list Sequence of positional arguments passed\",\n \"kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([]) #\",\n \"fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__\",\n \"func_hash(self): \\\"\\\"\\\" This returns the functional hash of a particular node. The default\",\n \"or None Parent graph of this node. If graph is `None`, this is\",\n \"f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self,\",\n \"var for var index {self} with variable shape {self.var.shape}\\\") return True else: return\",\n \"{self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain\",\n \"key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name\",\n \"f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name and self._name in graph.nodes:\",\n \"{self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name =\",\n \"list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other):\",\n \"target : callable function to evaluate the node args : tuple positional arguments\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other):\",\n \"value in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value\",\n \"graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return\",\n \"TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num\",\n \"isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict): return {partial(key):\",\n \"len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) !=\",\n \"self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return\",\n \"def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns\",\n \"def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift,\",\n \"name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self,\",\n \"# TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph =\",\n \"self._name = name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies\",\n \"def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift,\",\n \"else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self,\",\n \"None Shape of the output for a node. This can be a tuple\",\n \"__truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self)\",\n \"context : dict or None Context in which to evaluate the nodes. callback\",\n \"\\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value =\",\n \"name) else: graph.nodes[name] = self self._name = name return self def __getitem__(self, key):\",\n \"= nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ =\",\n \"context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes\",\n \"keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node =\",\n \"cannot be sliced for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape)\",\n \"nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all)\",\n \"list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not a list or numpy\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return\",\n \"inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift\",\n \"or `None` to use the default graph. Raises ------ ValueError If no `Graph`\",\n \"return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self,\",\n \"= [partial(arg) for arg in self.args] kwargs = {key: partial(value) for key, value\",\n \"node and discard the values. Parameters ---------- context : dict Normalised context in\",\n \"context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom =\",\n \"**kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\"\",\n \"def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" %\",\n \"Set the name for a variable index, making sure to replicate the new\",\n \"\\\"\\\"\\\" Set the name of the node and update the graph. Parameters ----------\",\n \"self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs +=\",\n \"graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self,\",\n \"nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub)\",\n \"def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred)\",\n \"def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1,\",\n \"slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx\",\n \"def set_name(self, name): \\\"\\\"\\\" Set the name for a variable index, making sure\",\n \"when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target,\",\n \"= nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ =\",\n \"i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var\",\n \"domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return\",\n \"other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self)\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if\",\n \"node args : tuple positional arguments passed to the target kwargs : dict\",\n \"kwargs : dict keywoard arguments passed to the target \\\"\\\"\\\" def __init__(self, target,\",\n \"add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE:\",\n \"\\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx =\",\n \": tuple Positional arguments passed to the `_evaluate` method. name : str or\",\n \"`value`. kwargs : dict Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self,\",\n \"return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname)\",\n \"graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ =\",\n \"nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property)\",\n \"or more `Node` instances or names to evaluate. context : dict or None\",\n \"name): \\\"\\\"\\\" Set the name of the node and update the graph. Parameters\",\n \"index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior\",\n \"!= len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in\",\n \"\\\"\\\"\\\" return self._shape @property def var(self): return self @property def name(self): \\\"\\\"\\\"str :\",\n \"return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def\",\n \"if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] =\",\n \"nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not\",\n \"and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index {self} with variable\",\n \"nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr)\",\n \"= nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ =\",\n \"n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]),\",\n \"when an node is evaluated. Returns ------- value : object Output of the\",\n \"in fname: continue # Stop tracing at the last interactive cell if interactive\",\n \"def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real,\",\n \"slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def\",\n \"nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem)\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph)\",\n \"= nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert =\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph)\",\n \"nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir)\",\n \"by retrieving the node object associated with the node name. Parameters ---------- node\",\n \"Parameters ---------- context : dict[Node or str, object] Context whose keys are node\",\n \"Raises ------ ValueError If `fetches` is not an `Node` instance, node name, or\",\n \"(str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single =\",\n \"self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else:\",\n \"kwargs = {key: partial(value) for key, value in self.kwargs.items() if key not in\",\n \"return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other):\",\n \"at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args,\",\n \"\\\"\\\"\\\" Evaluate an node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1\",\n \"Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name)\",\n \"slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs:\",\n \"func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed\",\n \"in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for\",\n \"= nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter =\",\n \"len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_\",\n \"= res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other,\",\n \"the top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds def succs(self): return\",\n \"elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) ==\",\n \"arguments passed to `func`. kwargs : dict Mapping of keyword arguments passed to\",\n \"context. Parameters ---------- context : dict Normalised context in which to evaluate the\",\n \"other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__,\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other,\",\n \"associated graph. KeyError If the current name of the node cannot be found\",\n \"the default graph. Raises ------ ValueError If no `Graph` instance can be obtained.\",\n \"def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s'\",\n \"= nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ =\",\n \"to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that\",\n \"import numpy as np import importlib from .graph import Graph from .domain import\",\n \"Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_\",\n \"\\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack)\",\n \"self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def\",\n \"np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is\",\n \"if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other,\",\n \"= nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ =\",\n \"0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch\",\n \"node is not equal to the current ndoe as ewll if self.graph and\",\n \"self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx\",\n \"else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"node. Returns ------- self : Node This node. Raises ------ ValueError If an\",\n \"node is executed. args : list Sequence of positional arguments passed to `func`.\",\n \"Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack =\",\n \"for s in self.shape: if not isinstance(s, Integral): return False return True def\",\n \"self.dependencies = [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) #\",\n \"@property def args(self): \\\"\\\"\\\" tuple : Positional arguments which are used for executing\",\n \"yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return\",\n \"= f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def\",\n \"succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def\",\n \"nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to check for finalzied shape\",\n \"not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k))\",\n \"def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv,\",\n \"EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if\",\n \"return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def\",\n \"callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value\",\n \"self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape\",\n \"dict Normalised context in which to evaluate the node. callback : callable or\",\n \"% \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes\",\n \"ValueError If `node` is not an `Node` instance or an node name. RuntimeError\",\n \"other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"def argnames(self): return [a.name if isinstance(a, Node) else a for a in self.args]\",\n \"ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context) # Add the keyword arguments\",\n \"f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not\",\n \"function returns a hash of all attributes and subgraphs of a node. \\\"\\\"\\\"\",\n \"exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_\",\n \"domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args,\",\n \"self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if\",\n \"isinstance(a, Node) else a for a in self.args] @property def shape(self): \\\"\\\"\\\" tuple\",\n \"for creating nodes from functions. \\\"\\\"\\\" # This is called when the decorator\",\n \"@property def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return self._name @property\",\n \"import importlib from .graph import Graph from .domain import Domain from .util import\",\n \"a context. Parameters ---------- context : dict Normalised context in which to evaluate\",\n \"\\\"\\\"\\\" Evaluate the node given a context. Parameters ---------- context : dict Normalised\",\n \"truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self,\",\n \"in fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def\",\n \"we can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other,\",\n \"= nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ =\",\n \"graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def __getitem__(self,\",\n \"def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift,\",\n \"min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_\",\n \"are node instances or names. kwargs : dict[str, object] Additional context information keyed\",\n \"update the graph. Parameters ---------- value : str Unique name of the node.\",\n \"are node instances. Raises ------ ValueError If the context specifies more than one\",\n \"def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return\",\n \"= self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\"\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return\",\n \"if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain =\",\n \"= self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs)\",\n \"or numpy array, and cannot be sliced for {self.name}\\\") elif isinstance(var, list): var\",\n \"if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name,\",\n \"del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help)\",\n \"for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr\",\n \"= \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)]\",\n \"name): g = self.graph while g is not None and name not in\",\n \"node is evaluated. Returns ------- value : object Output of the node given\",\n \"instance or name of an node. Returns ------- instantiated_node : Node Node instance.\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if\",\n \"Positional arguments passed to the `_evaluate` method. name : str or None Name\",\n \"\\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif\",\n \"= nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne =\",\n \"return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an\",\n \"\\\"\\\"\\\" Node representing values of a variable corresponding to input index values. Parameters\",\n \"other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"in the associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO: Need\",\n \"neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def\",\n \"indexing into. idx : tuple Tuple of either integer values or index/index_op nodes.\",\n \"if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom\",\n \"the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph)\",\n \"contextlib import traceback import uuid import numpy as np import importlib from .graph\",\n \"= (1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices\",\n \"return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph)\",\n \"== \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value\",\n \"@property def shape(self): \\\"\\\"\\\" tuple : Shape of the output for a node.\",\n \"% (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on a\",\n \"@classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant given\",\n \"@property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph\",\n \"disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ---------- target : callable function\",\n \"to the `_evaluate` method. name : str or None Name of the node\",\n \"1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices\",\n \"_is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index))\",\n \"kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2:\",\n \"for attr in ['start', 'stop', 'step']]) return node except Exception as ex: #\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self, other,\",\n \"Parameters ---------- func : callable Function to call when the node is executed.\",\n \"= nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint =\",\n \"slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def\",\n \"find_node(self, name): g = self.graph while g is not None and name not\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not\",\n \"return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph)\",\n \"\\\"\\\"\\\" Evaluate one or more nodes given a dictionary of node names with\",\n \"var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain\",\n \"*args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d\",\n \"nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate)\",\n \"**kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\" # This is called\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not\",\n \"list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) >\",\n \"be sliced for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) !=\",\n \"slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def\",\n \"frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack\",\n \"Parameters ---------- var : Node The multi-dimensional variable used for indexing into. idx\",\n \"# Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return\",\n \"Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args,\",\n \"in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain =\",\n \"If graph is `None`, this is the top-level graph. \\\"\\\"\\\" return self._graph def\",\n \"**kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed before\",\n \"evaluated. Returns ------- value : object Output of the node given the context.\",\n \"dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return\",\n \"to this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if\",\n \"import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence,\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not\",\n \"return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph)\",\n \"pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n =\",\n \"domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list =\",\n \"or uuid.uuid4().hex # TODO: Need a way to check if the existing node\",\n \"lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod\",\n \"node given a context. Parameters ---------- context : dict Normalised context in which\",\n \"list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_\",\n \"indices)) res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if\",\n \"invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_\",\n \"context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node names with node\",\n \"if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes\",\n \"isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must\",\n \"def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs:\",\n \"slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def\",\n \"(len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape)\",\n \"{out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val -\",\n \"existing node is not equal to the current ndoe as ewll if self.graph\",\n \"node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all\",\n \"scope. Parameters ---------- dependencies : list Sequence of nodes to be evaluted before\",\n \"else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"**kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\"\",\n \"Parameters ---------- node : Node or str Node instance or name of an\",\n \"node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant given a context. \\\"\\\"\\\"\",\n \"callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs)\",\n \"must be a mapping.\\\") nodes = list(context) # Add the keyword arguments for\",\n \"rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth\",\n \"else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs)\",\n \"from .graph import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable,\",\n \"__ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx\",\n \"self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return\",\n \"Unique name of the node. Returns ------- self : Node This node. Raises\",\n \"# pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message =\",\n \"k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes =\",\n \": str Unique name of the node. Returns ------- self : Node This\",\n \"Add the keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node)\",\n \"elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0:\",\n \"= self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return\",\n \"nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals)\",\n \"corresponding to input index values. Parameters ---------- var : Node The multi-dimensional variable\",\n \"builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import functools from\",\n \"a tuple of integers or parameter node names. graph : Node or None\",\n \"cal The multi-dimensional variable used for indexing into. idx : tuple Tuple of\",\n \"else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"(k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items())\",\n \"Node or str Node instance or name of an node. Returns ------- instantiated_node\",\n \"more `Node` instances or names to evaluate. context : dict or None Context\",\n \"lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self,\",\n \"shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if\",\n \"\\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or\",\n \"kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target =\",\n \"= nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ =\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return\",\n \"is `None`, this is the top-level graph. op_name : str Operation name which\",\n \"Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context) # Add the\",\n \"context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more\",\n \"nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip)\",\n \"node or `None` to use a random, unique identifier. shape : tuple or\",\n \"*, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes given a dictionary of\",\n \"return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this\",\n \"x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape)\",\n \"other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return\",\n \"(list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else:\",\n \"------- value : object Output of the node given the context. \\\"\\\"\\\" #\",\n \"other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"= nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift =\",\n \"node and add the node to the graph self._name = None self.name =\",\n \"context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node and discard the values.\",\n \"# TODO: Need a way to check if the existing node is not\",\n \"for arg in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items()\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other):\",\n \"kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this to underscore private variable\",\n \"name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key,\",\n \"target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter def target(self,\",\n \"the node cannot be found in the associated graph. \\\"\\\"\\\" name = name\",\n \"names. \\\"\\\"\\\" return self._shape @property def var(self): return self @property def name(self): \\\"\\\"\\\"str\",\n \"elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not\",\n \"ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\"\",\n \"= kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs:\",\n \"var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and\",\n \"graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_\",\n \"not a list or numpy array, and cannot be sliced for {self.name}\\\") elif\",\n \"object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_\",\n \"the name for a variable index, making sure to replicate the new name\",\n \"abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_\",\n \"nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not\",\n \"isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture\",\n \"graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed before any nodes in this\",\n \"\\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key,\",\n \"be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape =\",\n \"**kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target\",\n \"if node in context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] =\",\n \"= len(self) for i in range(num): yield self[i] def __eq__(self, other): return hash(self)\",\n \"be set using `value`. kwargs : dict Keyword arguments passed to the `_evaluate`\",\n \"in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback)\",\n \"self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self,\",\n \"keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose\",\n \"Returns ------- value : object Output of the node given the context. \\\"\\\"\\\"\",\n \"value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is\",\n \"args : tuple Positional arguments passed to the `_evaluate` method. name : str\",\n \"len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else:\",\n \"does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with\",\n \"if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e)\",\n \"def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape ==\",\n \"graph instance by returning the explicitly given graph or using the default graph.\",\n \"str Unique name of the node. Returns ------- self : Node This node.\",\n \"self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other):\",\n \"name of the node. Returns ------- self : Node This node. Raises ------\",\n \"node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one\",\n \"'polymath' in fname: continue # Stop tracing at the last interactive cell if\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self,\",\n \"float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape\",\n \"= nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul =\",\n \"return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph)\",\n \"nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not\",\n \"other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other,\",\n \"attr in ['start', 'stop', 'step']]) return node except Exception as ex: # pragma:\",\n \"the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph\",\n \"tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx,\",\n \"return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other,\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return\",\n \"= np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape)\",\n \"an node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if\",\n \"graph of this node. If graph is `None`, this is the top-level graph.\",\n \"__getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\":\",\n \"name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in\",\n \"for c in context: if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\",\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def\",\n \"This can be a tuple of integers or parameter node names. \\\"\\\"\\\" return\",\n \"None Name of the node or `None` to use a random, unique identifier.\",\n \"def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod,\",\n \"context in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ----------\",\n \"slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx =\",\n \"dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the\",\n \"slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def\",\n \"equal to the current ndoe as ewll if self.graph and name in self.graph.nodes:\",\n \"dict Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack\",\n \"if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the\",\n \"self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other):\",\n \"\\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in\",\n \"= f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\"\",\n \"nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float)\",\n \"traces fname = frame.filename if 'polymath' in fname: continue # Stop tracing at\",\n \"x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single\",\n \"= nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ =\",\n \"all `dependencies` are executed before any nodes in this scope. Parameters ---------- dependencies\",\n \"use a random, unique identifier. shape : tuple or None Shape of the\",\n \"isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else:\",\n \"iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_\",\n \"input index values. Parameters ---------- var : Node The multi-dimensional variable used for\",\n \"of an node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError\",\n \"dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ =\",\n \"old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else\",\n \"Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif\",\n \"any nodes in this scope. Parameters ---------- dependencies : list Sequence of nodes\",\n \"= nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod =\",\n \"f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if\",\n \"\\\"\\\"\\\" Evaluate the dependencies of this node and discard the values. Parameters ----------\",\n \"= nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le =\",\n \"in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self\",\n \"\\\"\\\"\\\" Set the name for a variable index, making sure to replicate the\",\n \"= name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the\",\n \"to work self._shape = UNSET_SHAPE else: shapes = [] for dim in shape:\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph)\",\n \"TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph = graph\",\n \"callback : callable or None Callback to be evaluated when an node is\",\n \"var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else:\",\n \"nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum)\",\n \"def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional arguments which\",\n \"no `Graph` instance can be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return\",\n \"raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\"\",\n \"slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on a node. Parameters ---------- target\",\n \"nodes given the context. Raises ------ ValueError If `fetches` is not an `Node`\",\n \"Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not\",\n \"is the top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds def succs(self):\",\n \": list Sequence of nodes to be evaluted before evaluating any nodes defined\",\n \"and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack\",\n \"their values. .. note:: This function modifies the context in place. Use :code:`context=context.copy()`\",\n \"values : Node or tuple[object] Output of the nodes given the context. Raises\",\n \"return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\")\",\n \"self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other):\",\n \"return self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield self[i]\",\n \"int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_\",\n \"[fetches] single = True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches`\",\n \": dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs)\",\n \"dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_\",\n \"The multi-dimensional variable used for indexing into. idx : tuple Tuple of either\",\n \"graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var,\",\n \"[] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float):\",\n \"args : tuple positional arguments passed to the target kwargs : dict keywoard\",\n \"returns an object id, whereas this function returns a hash of all attributes\",\n \"__mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \": Operation name which describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def\",\n \"val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not\",\n \"elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]),\",\n \"to evaluate the node. callback : callable or None Callback to be evaluated\",\n \"= op_name # Get the stack context so we can report where the\",\n \"range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var)\",\n \"frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_\",\n \"== UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral): return\",\n \"def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args return var\",\n \"def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func,\",\n \"value = context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal\",\n \"context = {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\")\",\n \"context, callback=None): \\\"\\\"\\\" Evaluate the node given a context. Parameters ---------- context :\",\n \"else x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx in\",\n \"__init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else\",\n \"the node cannot be found in the associated graph. \\\"\\\"\\\" # TODO: Need\",\n \"== DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices\",\n \"must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num =\",\n \"def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return\",\n \"memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter\",\n \"to return or `None` to use the default graph. Raises ------ ValueError If\",\n \"elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context)\",\n \"shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for\",\n \"= self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self)\",\n \"other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self)\",\n \"np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does\",\n \"target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in\",\n \"= nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul =\",\n \"if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape)\",\n \"def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed before any nodes\",\n \"op_name(self): \\\"\\\"\\\" str : Operation name which describes the node functionality. \\\"\\\"\\\" return\",\n \"= functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs =\",\n \"this scope. \\\"\\\"\\\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies)\",\n \"a variable corresponding to input index values. Parameters ---------- var : Node The\",\n \"other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other,\",\n \"idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var,\",\n \"return node raise ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\") def\",\n \"deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args,\",\n \"[self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context:\",\n \"'{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and\",\n \"shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if\",\n \"kwargs : dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args,\",\n \"Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 =\",\n \"nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool)\",\n \"sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_\",\n \"def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name\",\n \"ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\") def instantiate_graph(self, context, **kwargs):\",\n \"isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not a list\",\n \"\\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value\",\n \"if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value\",\n \"underscore private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape\",\n \"other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"[] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name]\",\n \"to the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name =\",\n \"given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return\",\n \"return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def\",\n \"def __len__(self): #TODO: Update this to check for finalzied shape if self.shape ==\",\n \"a node. Parameters ---------- target : cal The multi-dimensional variable used for indexing\",\n \"return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph)\",\n \"`Node` instance or an node name. RuntimeError If `node` is an `Node` instance\",\n \"or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub\",\n \"idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res =\",\n \"= nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth =\",\n \"g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\") def\",\n \"else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx =\",\n \"name with a unique stringwhich corresponds to the variable, index combination. Parameters ----------\",\n \"passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure\",\n \"np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx] for idx\",\n \"= fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self\",\n \"and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or\",\n \"nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr)\",\n \"callable or None Callback to be evaluated when an node is evaluated. Returns\",\n \"in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name\",\n \"is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape: if not\",\n \"target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator\",\n \"def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge,\",\n \"nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem)\",\n \"indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are\",\n \"a particular node. The default hash returns an object id, whereas this function\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other,\",\n \"= nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ =\",\n \"other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self,\",\n \"shape or tuple([]) # Get a list of all dependencies relevant to this\",\n \"scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return\",\n \"target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list)\",\n \"integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\"\",\n \"op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1],\",\n \"all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) >\",\n \"= [node] for n in node: stack = [] if isinstance(n, Node): for\",\n \"nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray)\",\n \"isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1,\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return\",\n \"globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_\",\n \"slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def\",\n \"isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float):\",\n \"graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return\",\n \"attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem\",\n \"name) else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context,\",\n \"kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain\",\n \"graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):]\",\n \"nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift)\",\n \"context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key: partial(value)\",\n \"other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"== 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) !=\",\n \"key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret\",\n \"`func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all\",\n \"node given the context. \\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback)\",\n \"if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1\",\n \"interactive = False if isinstance(node, Node) or not is_iterable(node): node = [node] for\",\n \"with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val\",\n \"in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does not belong to\",\n \"self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args return var def set_name(self, name):\",\n \"= Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa\",\n \"*args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and keyword arguments `kwargs`.\",\n \"whose keys are node instances. Raises ------ ValueError If the context specifies more\",\n \"if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret =\",\n \"__repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing\",\n \"is not an `Node` instance, node name, or a sequence thereof. \\\"\\\"\\\" if\",\n \"g is not None and name not in g.nodes: g = g.graph if\",\n \"assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self,\",\n \"def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape: if\",\n \"message = \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message)\",\n \"graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return\",\n \"before evaluating any nodes defined in this scope. \\\"\\\"\\\" # Add dependencies to\",\n \"= self self._name = name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph)\",\n \"= tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by\",\n \"other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self)\",\n \"else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral,\",\n \"`node` is not an `Node` instance or an node name. RuntimeError If `node`\",\n \": dict[Node or str, object] Context whose keys are node instances or names.\",\n \"var, idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain =\",\n \"graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return\",\n \"concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq\",\n \"and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and\",\n \"== hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if\",\n \"= [] self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = []\",\n \"Decorator for creating nodes from functions. \\\"\\\"\\\" # This is called when the\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other):\",\n \"deque import functools from numbers import Integral, Rational, Real import contextlib import traceback\",\n \"else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self,\",\n \"else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"be evaluated when an node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context,\",\n \"def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903\",\n \"def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift,\",\n \"self._name = None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name =\",\n \"not an `Node` instance, node name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches,\",\n \"(n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None,\",\n \"is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0]\",\n \"# pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list)\",\n \"def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow,\",\n \"if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname)\",\n \"val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)\",\n \"# This is called when the decorator is used with arguments if target\",\n \"c in context: if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]:\",\n \": dict keywoard arguments passed to the target \\\"\\\"\\\" def __init__(self, target, *args,\",\n \"else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any\",\n \"on a node. Parameters ---------- target : cal The multi-dimensional variable used for\",\n \"@shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph)\",\n \"== old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx):\",\n \"!= DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var\",\n \"self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self):\",\n \"node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"]\",\n \"if context is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context`\",\n \"{key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in\",\n \"self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name]\",\n \"report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\"\",\n \"variable corresponding to input index values. Parameters ---------- var : Node The multi-dimensional\",\n \"or Node] or str or Node One or more `Node` instances or names\",\n \"f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not\",\n \"nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter)\",\n \"= nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ =\",\n \"domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args\",\n \"return or `None` to use the default graph. Raises ------ ValueError If no\",\n \"private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape =\",\n \"be found in the associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex #\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other):\",\n \"return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n\",\n \"return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self,\",\n \"self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >=\",\n \"__getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the\",\n \"nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod)\",\n \"return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def\",\n \"to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing\",\n \"ValueError(f\\\"Invalid shape var for var index {self} with variable shape {self.var.shape}\\\") return True\",\n \"self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in\",\n \"or len(shape) == 0: # TODO: Change in order to enable \\\"is shape\",\n \"`_evaluate` method. name : str or None Name of the node or `None`\",\n \"list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs):\",\n \"__lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other,\",\n \"tracing at the last interactive cell if interactive and not fname.startswith('<'): break #\",\n \"op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return\",\n \"= nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift =\",\n \"be a tuple of integers or parameter node names. graph : Node or\",\n \"a node has a default value to use for execution, it can be\",\n \"work self._shape = UNSET_SHAPE else: shapes = [] for dim in shape: if\",\n \"**kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement\",\n \"== (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res\",\n \"in order to enable \\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE else:\",\n \"\\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"or None Graph to return or `None` to use the default graph. Raises\",\n \"= [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim,\",\n \"arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx =\",\n \"def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other,\",\n \"self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,)\",\n \"retrieving the node object associated with the node name. Parameters ---------- node :\",\n \"import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object):\",\n \"n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This\",\n \"if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this to\",\n \"node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node\",\n \"self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def\",\n \"init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0:\",\n \"graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self):\",\n \"f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the\",\n \"tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for element in\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph)\",\n \"__abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self,\",\n \"isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain,\",\n \"name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name}\",\n \"instance or an node name. RuntimeError If `node` is an `Node` instance but\",\n \"callable Function to call when the node is executed. args : list Sequence\",\n \"return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def\",\n \"context : dict[Node or str, object] Context whose keys are node instances or\",\n \"= self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name)\",\n \"for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and\",\n \"graph is `None`, this is the top-level graph. \\\"\\\"\\\" return self._graph def preds(self):\",\n \"str, object] Context whose keys are node instances or names. kwargs : dict[str,\",\n \"Parameters ---------- dependencies : list Sequence of nodes to be evaluted before evaluating\",\n \"__ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self,\",\n \"Raises ------ ValueError If `node` is not an `Node` instance or an node\",\n \"self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class\",\n \"tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)):\",\n \"\\\"\\\"\\\" This returns the functional hash of a particular node. The default hash\",\n \"list does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name}\",\n \"= slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name,\",\n \"= self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def\",\n \"an node with `value` already exists in the associated graph. KeyError If the\",\n \"set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat\",\n \"self.graph: graph = self.graph if self._name is not None and self._name in graph.nodes:\",\n \"by returning the explicitly given graph or using the default graph. Parameters ----------\",\n \"nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type)\",\n \"self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key,\",\n \"name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"not equal to the current ndoe as ewll if self.graph and name in\",\n \"other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other):\",\n \"value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors = []\",\n \"\\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name)\",\n \"eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_\",\n \"shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def\",\n \"matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other):\",\n \"isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0: #\",\n \"domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return\",\n \"= nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt =\",\n \"nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift)\",\n \"self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else:\",\n \"other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self)\",\n \"stateless functions. Parameters ---------- target : callable function to evaluate the node args\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other,\",\n \"isinstance(node, Node) or not is_iterable(node): node = [node] for n in node: stack\",\n \"the node given a context. Parameters ---------- context : dict Normalised context in\",\n \"round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_\",\n \"if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid\",\n \"polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`, this is\",\n \"self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape of the output for a\",\n \"be an `Node` instance, node name, or a \\\" \\\"sequence thereof.\\\") fetches =\",\n \"domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self,\",\n \"functools.partial(nodeop, **kwargs) # This is called when the decorator is used without arguments\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self,\",\n \"= nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf =\",\n \"given the context. Raises ------ ValueError If `fetches` is not an `Node` instance,\",\n \"nodes from functions. \\\"\\\"\\\" # This is called when the decorator is used\",\n \"graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return\",\n \"Node instance or name of an node. Returns ------- instantiated_node : Node Node\",\n \"isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx] for\",\n \"shape or len(shape) == 0: # TODO: Change in order to enable \\\"is\",\n \"If the current name of the node cannot be found in the associated\",\n \"True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var,\",\n \"@property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None\",\n \"uuid.uuid4().hex # TODO: Need a way to check if the existing node is\",\n \"parameter node names. graph : Node or None Parent graph of this node.\",\n \"= UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim, (Node,\",\n \"self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property\",\n \"[key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple,\",\n \"more nodes given a dictionary of node names with their values. .. note::\",\n \"This can be a tuple of integers or parameter node names. graph :\",\n \"if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing:\",\n \"len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\" #\",\n \"Node This node. Raises ------ ValueError If an node with `value` already exists\",\n \">= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C')\",\n \"if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does\",\n \": dict[str, object] Additional context information keyed by variable name. Returns ------- normalized_context\",\n \"var : Node The multi-dimensional variable used for indexing into. idx : tuple\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self,\",\n \"1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1\",\n \"return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\") def __len__(self):\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self,\",\n \"None Parent graph of this node. If graph is `None`, this is the\",\n \"or using the default graph. Parameters ---------- graph : Node or None Graph\",\n \"s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray)\",\n \"= nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ =\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if\",\n \"nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_ = nodeop(importlib.import_module)\",\n \"graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self,\",\n \"the output for a node. This can be a tuple of integers or\",\n \"indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow\",\n \"id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional hash of a particular node.\",\n \"= nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ =\",\n \"by variable name. Returns ------- values : Node or tuple[object] Output of the\",\n \"name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\")\",\n \"other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other,\",\n \"self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self,\",\n \"\\\"\\\"\\\" Obtain the currently active graph instance by returning the explicitly given graph\",\n \"else: shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim)\",\n \"_is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args :\",\n \"\\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs)\",\n \"operator from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import\",\n \"identifier. shape : tuple or None Shape of the output for a node.\",\n \"from functions. \\\"\\\"\\\" # This is called when the decorator is used with\",\n \"Node or None Parent graph of this node. If graph is `None`, this\",\n \"dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = []\",\n \"= Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes\",\n \"tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and\",\n \"return [partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value) for\",\n \"nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int)\",\n \"\\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"= nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter =\",\n \"self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self,\",\n \"ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos\",\n \"(self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions.\",\n \"given a context. Parameters ---------- context : dict Normalised context in which to\",\n \"isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx =\",\n \"if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var,\",\n \"None: return functools.partial(nodeop, **kwargs) # This is called when the decorator is used\",\n \"# Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove\",\n \"if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def\",\n \"TypeError(f\\\"Variable {var} with type {type(var)} is not a list or numpy array, and\",\n \"nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg)\",\n \"nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul)\",\n \"= kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or tuple([])\",\n \"self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self,\",\n \"= nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ =\",\n \"def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()}\",\n \"[key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if\",\n \"than one value for any node. ValueError If `context` is not a mapping.\",\n \"!= len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape}\",\n \"1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add,\",\n \"stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None,\",\n \"or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing\",\n \"key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return\",\n \"node names with node instances. .. note:: This function modifies the context in\",\n \"var(self): return self @property def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\"\",\n \"self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def\",\n \"for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i in\",\n \"__xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other,\",\n \"\\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from\",\n \"Callback to be evaluated when an node is evaluated. Returns ------- value :\",\n \"args : list Sequence of positional arguments passed to `func`. kwargs : dict\",\n \"shape : tuple or None Shape of the output for a node. This\",\n \"whereas this function returns a hash of all attributes and subgraphs of a\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self,\",\n \"return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data):\",\n \"class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable corresponding\",\n \"(tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes)\",\n \"ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple):\",\n \"when the decorator is used with arguments if target is None: return functools.partial(nodeop,\",\n \"slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var,\",\n \"str : Operation name which describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter\",\n \"isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must be an `Node` instance,\",\n \"= self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value\",\n \"\\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\"\",\n \"can be set using `value`. kwargs : dict Keyword arguments passed to the\",\n \"None Callback to be evaluated when an node is evaluated. \\\"\\\"\\\" for node\",\n \"graph. \\\"\\\"\\\" # TODO: Need a way to check if the existing node\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other,\",\n \"an `Node` instance but does not belong to this graph. \\\"\\\"\\\" if isinstance(node,\",\n \"context information keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized\",\n \"False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)])\",\n \"nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod)\",\n \"for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\"\",\n \"# Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for\",\n \"target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self):\",\n \"(len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index\",\n \"in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) ==\",\n \"nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow)\",\n \"def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph =\",\n \"is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the\",\n \"**kwargs) for c in context: if c in fetches and c.op_name in [\\\"output\\\",\",\n \"names with their values. .. note:: This function modifies the context in place.\",\n \"if self in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node,\",\n \"\\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"\\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable\",\n \"= self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var])\",\n \"def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add,\",\n \"self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key,\",\n \"node functionality. value : Any or None If a node has a default\",\n \"self._shape = tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change\",\n \"pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed\",\n \"idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key,\",\n \"idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape\",\n \"__call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s\",\n \"def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return\",\n \"\\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max)\",\n \"Node Node instance. Raises ------ ValueError If `node` is not an `Node` instance\",\n \"= self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self)\",\n \"and add the node to the graph self._name = None self.name = name\",\n \"= False else: raise ValueError(\\\"`fetches` must be an `Node` instance, node name, or\",\n \"keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name = [] if isinstance(key,\",\n \"self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self,\",\n \"UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def\",\n \"or more nodes given a dictionary of node names with their values. ..\",\n \"return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def\",\n \"\\\"target\\\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc\",\n \"**kwargs): \\\"\\\"\\\" Evaluate an node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes +=\",\n \"instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object associated\",\n \"or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node):\",\n \"for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly\",\n \"nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print)\",\n \"function modifies the context in place. Use :code:`context=context.copy()` to avoid the context being\",\n \"{Node._eval_stack}\\\") if not single and not all([(idx_val - 1) >= indices[-1][idx] for idx,\",\n \"tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target,\",\n \"def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor,\",\n \"or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None,\",\n \"_flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters\",\n \"'{node}' does not belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return\",\n \"return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args return var def set_name(self,\",\n \"raise ValueError(\\\"`fetches` must be an `Node` instance, node name, or a \\\" \\\"sequence\",\n \": callable or None Callback to be evaluated when an node is evaluated.\",\n \"for stateless functions. Parameters ---------- target : callable function to evaluate the node\",\n \"self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"to replicate the new name with a unique stringwhich corresponds to the variable,\",\n \"if isinstance(n, Node): for frame in reversed(n._stack): # pylint: disable=protected-access # Do not\",\n \"c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback)\",\n \"== 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return\",\n \"UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque\",\n \"= nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf =\",\n \"\\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) +\",\n \"other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other,\",\n \"def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and\",\n \"args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for\",\n \"raise TypeError(f'`shape` must be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self):\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other):\",\n \"+ \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)):\",\n \"tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph =\",\n \"self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self,\",\n \"name of the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str : Operation\",\n \"tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape) ==\",\n \"where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other):\",\n \"slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name\",\n \"return self._name @property def op_name(self): \\\"\\\"\\\" str : Operation name which describes the\",\n \"def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k ==\",\n \"return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph)\",\n \"= target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple,\",\n \"@target.setter def target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\"\",\n \"return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif\",\n \"context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\"\",\n \": Any or None If a node has a default value to use\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self,\",\n \"names with node instances. .. note:: This function modifies the context in place.\",\n \"other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other,\",\n \"node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object associated with\",\n \"= value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors =\",\n \"an node is evaluated. Returns ------- value : object Output of the node\",\n \"**kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args = []\",\n \"Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to check for finalzied shape if\",\n \"instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node names with\",\n \"not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index\",\n \"nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow)\",\n \"def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"\\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE else: shapes = [] for\",\n \"cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate\",\n \"__rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"= nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub =\",\n \"Get the stack context so we can report where the node was defined\",\n \"for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the\",\n \"a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches] single =\",\n \"lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self,\",\n \"not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name\",\n \"truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self,\",\n \"return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph)\",\n \"replacing all node names with node instances. .. note:: This function modifies the\",\n \"[\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name,\",\n \"= self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes\",\n \"def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def\",\n \"other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self)\",\n \"c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches]\",\n \"unique stringwhich corresponds to the variable, index combination. Parameters ---------- value : str\",\n \"dict[Node, object] Normalized context whose keys are node instances. Raises ------ ValueError If\",\n \"top-level graph. op_name : str Operation name which describes the node functionality. value\",\n \"Operation name which describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self,\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other):\",\n \"evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node and discard the\",\n \"def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0]\",\n \"def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by returning the explicitly\",\n \"specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for\",\n \"if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\",\",\n \"self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should\",\n \"evaluate the node args : tuple positional arguments passed to the target kwargs\",\n \"not belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node raise\",\n \"graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return\",\n \"else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self):\",\n \"return functools.partial(nodeop, **kwargs) # This is called when the decorator is used without\",\n \"context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] = value if node.op_name\",\n \"graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda\",\n \"graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return\",\n \"nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance)\",\n \"node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\":\",\n \"return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def\",\n \"super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property\",\n \"hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_\",\n \"== \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape):\",\n \"KeyError If the current name of the node cannot be found in the\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if\",\n \"If `context` is not a mapping. \\\"\\\"\\\" if context is None: context =\",\n \"Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\"\",\n \"ValueError If the context specifies more than one value for any node. ValueError\",\n \"node self.dependencies = [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies)\",\n \"__getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key =\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other,\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self, other,\",\n \"arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop\",\n \"slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in\",\n \"0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)):\",\n \"\\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key,\",\n \"or tuple([]) # Get a list of all dependencies relevant to this node\",\n \"elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must be an `Node`\",\n \"= nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv =\",\n \"if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 =\",\n \"hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in key: if isinstance(k, Node):\",\n \"{self.name} has indices which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar\",\n \"__sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other,\",\n \"in self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape of the output for\",\n \"the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name\",\n \"from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict)\",\n \"bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_\",\n \"= nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv =\",\n \"cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args):\",\n \"value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies,\",\n \"return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph)\",\n \"value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start',\",\n \"\\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this to underscore\",\n \"return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def\",\n \"in ['start', 'stop', 'step']]) return node except Exception as ex: # pragma: no\",\n \"arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node)\",\n \"a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context,\",\n \"graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self):\",\n \"in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name\",\n \"element in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in\",\n \"node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If `node`\",\n \"a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches] context =\",\n \"idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\"))\",\n \"a variable index, making sure to replicate the new name with a unique\",\n \"else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if\",\n \"graph=self) return ret def is_scalar(self, val=None): if val is not None and (not\",\n \"pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate\",\n \"= self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom)\",\n \"the context being modified. Parameters ---------- fetches : list[str or Node] or str\",\n \"ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}'\",\n \"return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key,\",\n \"callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes given a dictionary of node\",\n \"is not None and name not in g.nodes: g = g.graph if name\",\n \"else: name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name\",\n \"Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional arguments which are used for\",\n \"graph : Node or None Parent graph of this node. If graph is\",\n \"nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars)\",\n \"any node. ValueError If `context` is not a mapping. \\\"\\\"\\\" if context is\",\n \"self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function\",\n \"nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr)\",\n \"def __iter__(self): num = len(self) for i in range(num): yield self[i] def __eq__(self,\",\n \"not is_iterable(node): node = [node] for n in node: stack = [] if\",\n \"else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args):\",\n \"------ ValueError If an node with `value` already exists in the associated graph.\",\n \"numpy array, and cannot be sliced for {self.name}\\\") elif isinstance(var, list): var =\",\n \"= var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not\",\n \"explicitly given graph or using the default graph. Parameters ---------- graph : Node\",\n \"node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod\",\n \"pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self,\",\n \"import traceback import uuid import numpy as np import importlib from .graph import\",\n \"for any node. ValueError If `context` is not a mapping. \\\"\\\"\\\" if context\",\n \"and not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name) else:\",\n \"if 'polymath' in fname: continue # Stop tracing at the last interactive cell\",\n \"= context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value\",\n \"value : object Output of the node given the context. \\\"\\\"\\\" # Evaluate\",\n \"graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node\",\n \"def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif\",\n \"the node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ ==\",\n \"single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False\",\n \"from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral,\",\n \"def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"node_hash(self) def find_node(self, name): g = self.graph while g is not None and\",\n \"0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d\",\n \"for i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name]\",\n \"[node] for n in node: stack = [] if isinstance(n, Node): for frame\",\n \"= name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape):\",\n \"of integers or parameter node names. \\\"\\\"\\\" return self._shape @property def var(self): return\",\n \"val=None): if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) ==\",\n \"self.added_attrs += [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter def target(self, fnc):\",\n \"must be an `Node` instance, node name, or a \\\" \\\"sequence thereof.\\\") fetches\",\n \"= nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ =\",\n \"not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack =\",\n \"- {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\")\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other,\",\n \"index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on\",\n \"context being modified. Parameters ---------- context : dict[Node or str, object] Context whose\",\n \"name. Parameters ---------- node : Node or str Node instance or name of\",\n \"= cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert\",\n \"slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class\",\n \"disable=protected-access # Do not capture any internal stack traces fname = frame.filename if\",\n \"write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name\",\n \"[\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target =\",\n \"arg in self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if\",\n \"op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args\",\n \"callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self]\",\n \"with variable shape {self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self,\",\n \"graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_\",\n \"nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not)\",\n \"index_list = self.args return var def set_name(self, name): \\\"\\\"\\\" Set the name for\",\n \"ValueError(f\\\"var_index {self.name} has indices which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\"\",\n \"\\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx =\",\n \"**kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element\",\n \"list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0],\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if\",\n \"str): for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key)\",\n \"avoid the context being modified. Parameters ---------- context : dict[Node or str, object]\",\n \"xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self,\",\n \"return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class\",\n \"return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def\",\n \"% (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None,\",\n \"def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape):\",\n \"= nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ =\",\n \"\\\"\\\"\\\" # This is called when the decorator is used with arguments if\",\n \"def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if\",\n \"class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ----------\",\n \"in this scope. Parameters ---------- dependencies : list Sequence of nodes to be\",\n \"nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable)\",\n \"the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str : Operation name which\",\n \"an `Node` instance, node name, or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node)\",\n \"self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx\",\n \"collections import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational,\",\n \"shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def\",\n \"Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return\",\n \"fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s` defined\",\n \"single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices)\",\n \"elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op,\",\n \"the nodes. callback : callable or None Callback to be evaluated when an\",\n \"and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape,\",\n \"pragma: no cover messages = [] interactive = False if isinstance(node, Node) or\",\n \"the existing node is not equal to the current ndoe as ewll if\",\n \"self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape\",\n \"__radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"describes the node functionality. value : Any or None If a node has\",\n \"a way to check if the existing node is not equal to the\",\n \"of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs):\",\n \"if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape\",\n \"isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float):\",\n \"to evaluate the node args : tuple positional arguments passed to the target\",\n \"__getitem__(self, key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None):\",\n \"not belong to this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node,\",\n \"scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self,\",\n \"for element in node) if isinstance(node, list): return [partial(element) for element in node]\",\n \"self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph:\",\n \"__mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other,\",\n \"+= 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node,\",\n \": object Output of the node given the context. \\\"\\\"\\\" # Evaluate all\",\n \"elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self,\",\n \"shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder or integer value for {self.name}\\\\n\\\"\",\n \"into. idx : tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\"\",\n \"Sequence of positional arguments passed to `func`. kwargs : dict Mapping of keyword\",\n \"nodeop(operator.invert) ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_)\",\n \"of keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies,\",\n \"# Choose a name for the node and add the node to the\",\n \"nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str)\",\n \"is None: return functools.partial(nodeop, **kwargs) # This is called when the decorator is\",\n \"graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph\",\n \"*args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n\",\n \"Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self]\",\n \"the keyword arguments for node in nodes: # pylint:disable=W0621 value = context.pop(node) node\",\n \"val is not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and\",\n \"self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self,\",\n \"else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return\",\n \"name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if\",\n \"an node name. RuntimeError If `node` is an `Node` instance but does not\",\n \"in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given\",\n \"are executed before any nodes in this scope. Parameters ---------- dependencies : list\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self,\",\n \"tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def\",\n \"import OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real\",\n \"return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph)\",\n \"Choose a name for the node and add the node to the graph\",\n \"\\\"\\\"\\\" Instantiate a graph by replacing all node names with node instances. ..\",\n \"partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs\",\n \"__rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self,\",\n \"return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph)\",\n \"in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args,\",\n \"instance. Raises ------ ValueError If `node` is not an `Node` instance or an\",\n \"__pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other,\",\n \"if out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices)\",\n \"fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key):\",\n \"= nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ =\",\n \"return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)\",\n \"with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context,\",\n \"= nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor) import_ =\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return\",\n \"(list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name,\",\n \"Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial =\",\n \"nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i in range(num):\",\n \"self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors = [] self.args\",\n \"def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ !=\",\n \"self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else: out_shape =\",\n \"top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds def succs(self): return self._preds\",\n \"or str, object] Context whose keys are node instances or names. kwargs :\",\n \"\\\"__len__\\\") and not isinstance(key, str): for k in key: if isinstance(k, Node): name.append(k.name)\",\n \"self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] =\",\n \"to `func`. kwargs : dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\"\",\n \"def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral):\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return\",\n \"replicate the new name with a unique stringwhich corresponds to the variable, index\",\n \"when an node is evaluated. kwargs : dict Additional context information keyed by\",\n \"\\\"\\\"\\\" Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ----------\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self,\",\n \"in the associated graph. KeyError If the current name of the node cannot\",\n \"\\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args)\",\n \"for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return\",\n \"Parameters ---------- value : str Unique name of the node. Returns ------- self\",\n \"[\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context\",\n \"the functional hash of a particular node. The default hash returns an object\",\n \"nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod)\",\n \"_eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None,\",\n \"thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif\",\n \"self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name =\",\n \"return False for s in self.shape: if not isinstance(s, Integral): return False return\",\n \"place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context :\",\n \"name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self,\",\n \"== 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain\",\n \"raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\",\",\n \"**kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain\",\n \"positional arguments passed to `func`. kwargs : dict Mapping of keyword arguments passed\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other,\",\n \"executed. args : list Sequence of positional arguments passed to `func`. kwargs :\",\n \"= nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ =\",\n \"return self elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key\",\n \"= nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ =\",\n \"def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\"\",\n \"sure to replicate the new name with a unique stringwhich corresponds to the\",\n \"return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except Exception\",\n \"a unique stringwhich corresponds to the variable, index combination. Parameters ---------- value :\",\n \"in self.shape: if not isinstance(s, Integral): return False return True def set_shape(self, shape=None,\",\n \"single = False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not\",\n \"list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if\",\n \"domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index,\",\n \"= nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ =\",\n \"in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback =\",\n \"this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node)\",\n \"if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing:\",\n \"graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name,\",\n \"mapping.\\\") nodes = list(context) # Add the keyword arguments for node in nodes:\",\n \"name not in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name]\",\n \"__rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"to input index values. Parameters ---------- var : Node The multi-dimensional variable used\",\n \"evaluated when an node is evaluated. kwargs : dict Additional context information keyed\",\n \"op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key):\",\n \"_is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var,\",\n \"slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def\",\n \"\\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or\",\n \"of the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str : Operation name\",\n \"is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift\",\n \"name or uuid.uuid4().hex # TODO: Need a way to check if the existing\",\n \"other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self,\",\n \"return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape =\",\n \"ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph =\",\n \"Normalized context whose keys are node instances. Raises ------ ValueError If the context\",\n \"slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs:\",\n \"str Node instance or name of an node. Returns ------- instantiated_node : Node\",\n \"Positional arguments which are used for executing this node. \\\"\\\"\\\" return tuple(self._args) @property\",\n \"graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not\",\n \"__mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other,\",\n \"def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv,\",\n \"if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized()\",\n \"Raises ------ ValueError If the context specifies more than one value for any\",\n \"False if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray,\",\n \"floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self,\",\n \"mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self,\",\n \"instances. .. note:: This function modifies the context in place. Use :code:`context=context.copy()` to\",\n \"\\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return\",\n \"def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self,\",\n \"== np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] ==\",\n \"node = [node] for n in node: stack = [] if isinstance(n, Node):\",\n \"the context specifies more than one value for any node. ValueError If `context`\",\n \"shape var for var index {self} with variable shape {self.var.shape}\\\") return True else:\",\n \"bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices))\",\n \"fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in\",\n \"= graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a\",\n \"fetches : list[str or Node] or str or Node One or more `Node`\",\n \"= None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name\",\n \"and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else:\",\n \"for k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name\",\n \"if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"):\",\n \"Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are node instances.\",\n \"and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self,\",\n \"len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for\",\n \"is not an `Node` instance or an node name. RuntimeError If `node` is\",\n \"kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"],\",\n \"traceback import uuid import numpy as np import importlib from .graph import Graph\",\n \"else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda\",\n \"return var def set_name(self, name): \\\"\\\"\\\" Set the name for a variable index,\",\n \"single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]):\",\n \"gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph\",\n \"list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var,\",\n \"node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs):\",\n \"self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node]\",\n \"kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in\",\n \"other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self,\",\n \"slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain,\",\n \"= nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ =\",\n \"slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def\",\n \"of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None,\",\n \"If no `Graph` instance can be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1]\",\n \"key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif\",\n \"def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var,\",\n \"Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args : tuple Positional arguments\",\n \"def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key,\",\n \"isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and\",\n \"the associated graph. KeyError If the current name of the node cannot be\",\n \"ValueError(f\\\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice\",\n \"= \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise\",\n \"= 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes\",\n \"(self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on a node.\",\n \"Node): self._shape = tuple([shape]) elif not shape or len(shape) == 0: # TODO:\",\n \"def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt,\",\n \"messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None,\",\n \"return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional arguments which are used\",\n \"not a mapping. \\\"\\\"\\\" if context is None: context = {} elif not\",\n \"s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d,\",\n \"= shape or tuple([]) # Get a list of all dependencies relevant to\",\n \"not shape or len(shape) == 0: # TODO: Change in order to enable\",\n \"\\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items():\",\n \"sliced for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape)\",\n \"node. This can be a tuple of integers or parameter node names. \\\"\\\"\\\"\",\n \"if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return\",\n \"np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0:\",\n \"= Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self,\",\n \"if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for\",\n \"can be a tuple of integers or parameter node names. \\\"\\\"\\\" return self._shape\",\n \"\\\"\\\"\\\" Inheriting nodes should implement this function to evaluate the node. \\\"\\\"\\\" return\",\n \"range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name):\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other):\",\n \"**kwargs): return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\"\",\n \"nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add)\",\n \"self.value = value self.dependencies = [] self._args = [] self._predeecessors = [] self._succesors\",\n \"isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise\",\n \"slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa\",\n \"self @property def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return self._name\",\n \"information keyed by variable name. Returns ------- normalized_context : dict[Node, object] Normalized context\",\n \"ior = nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not\",\n \"uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack context so\",\n \"= np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)}\",\n \"`Node` instance, node name, or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for\",\n \"mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self,\",\n \"= tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape:\",\n \"object associated with the node name. Parameters ---------- node : Node or str\",\n \"nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format)\",\n \"0: # TODO: Change in order to enable \\\"is shape finalized\\\" to work\",\n \"graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return\",\n \"or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain\",\n \"if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else:\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if\",\n \"__lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name #\",\n \"classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_\",\n \"def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,)\",\n \"@classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args)\",\n \"Sequence, deque import functools from numbers import Integral, Rational, Real import contextlib import\",\n \"kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain\",\n \"is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] ==\",\n \"None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index))\",\n \"graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self):\",\n \"positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function\",\n \"\\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\" # This is called when\",\n \"is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"):\",\n \"node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent\",\n \"self._succesors = [] self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs =\",\n \"the target kwargs : dict keywoard arguments passed to the target \\\"\\\"\\\" def\",\n \"CHange this to underscore private variable self.kwargs = kwargs self.graph = graph self._shape\",\n \"slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def\",\n \"(new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n)\",\n \"self.shape: if not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False):\",\n \"_evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single\",\n \"nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin)\",\n \"\\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx\",\n \"{self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not\",\n \"in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if\",\n \"Context whose keys are node instances or names. kwargs : dict[str, object] Additional\",\n \"{self.graph}\\\") def __len__(self): #TODO: Update this to check for finalzied shape if self.shape\",\n \"return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def\",\n \"def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node):\",\n \"return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph)\",\n \"return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name\",\n \"in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape\",\n \"__init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"],\",\n \"\\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph)\",\n \"*args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else\",\n \"list[str or Node] or str or Node One or more `Node` instances or\",\n \"= nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq =\",\n \"node in context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] = value\",\n \"keys are node instances. Raises ------ ValueError If the context specifies more than\",\n \"from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs):\",\n \"dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value,\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not\",\n \"None Context in which to evaluate the nodes. callback : callable or None\",\n \"__repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\"\",\n \"context. \\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self in\",\n \"@nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and\",\n \"__ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"= slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self,\",\n \"Context in which to evaluate the nodes. callback : callable or None Callback\",\n \"isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single\",\n \"= OrderedDict() self.shape = shape or tuple([]) # Get a list of all\",\n \"graph by replacing all node names with node instances. .. note:: This function\",\n \"else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a list of\",\n \"!= np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\"\",\n \"isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple\",\n \"def is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray) or\",\n \"self.op_name = op_name # Get the stack context so we can report where\",\n \"if isinstance(a, Node) else a for a in self.args] @property def shape(self): \\\"\\\"\\\"\",\n \"self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other):\",\n \"slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([]))\",\n \"self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on a node. Parameters\",\n \"a tuple of integers or parameter node names. \\\"\\\"\\\" return self._shape @property def\",\n \"1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise\",\n \"given graph or using the default graph. Parameters ---------- graph : Node or\",\n \"to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is\",\n \": list[str or Node] or str or Node One or more `Node` instances\",\n \"return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph)\",\n \"return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node\",\n \"key not in self.added_attrs} # Evaluate the node callback = callback or _noop_callback\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not\",\n \"as np import importlib from .graph import Graph from .domain import Domain from\",\n \"Node representing multi-dimensional operations performed on a node. Parameters ---------- target : cal\",\n \"tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape)\",\n \"[self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def\",\n \"__le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"):\",\n \"other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other):\",\n \"return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" %\",\n \"isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([]))\",\n \"decorator is used with arguments if target is None: return functools.partial(nodeop, **kwargs) #\",\n \"= np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)):\",\n \"---------- context : dict Normalised context in which to evaluate the node. callback\",\n \"Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors = []\",\n \"other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other):\",\n \"# pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ---------- target :\",\n \"other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" %\",\n \"graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an\",\n \"self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom =\",\n \"str or None Name of the node or `None` to use a random,\",\n \"self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes)\",\n \"functions. Parameters ---------- target : callable function to evaluate the node args :\",\n \"ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf\",\n \"nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le)\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return\",\n \"corresponds to the variable, index combination. Parameters ---------- value : str Unique name\",\n \".. note:: This function modifies the context in place. Use :code:`context=context.copy()` to avoid\",\n \"name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches]\",\n \"other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self,\",\n \": dict Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None])\",\n \"= nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ =\",\n \"nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object)\",\n \"default hash returns an object id, whereas this function returns a hash of\",\n \"'%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\"\",\n \"tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change in order\",\n \"Sequence): single = False else: raise ValueError(\\\"`fetches` must be an `Node` instance, node\",\n \"graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies =\",\n \"random, unique identifier. shape : tuple or None Shape of the output for\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph)\",\n \"self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters\",\n \"passed to the `_evaluate` method. name : str or None Name of the\",\n \"- 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has\",\n \"partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for\",\n \"in context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] = value if\",\n \"'{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if\",\n \"interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node\",\n \"slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def\",\n \"return self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred):\",\n \"for node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if\",\n \"names to evaluate. context : dict or None Context in which to evaluate\",\n \"Update this to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape`\",\n \"disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable corresponding to input index values.\",\n \"\\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other,\",\n \"self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0:\",\n \"def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e\",\n \"graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return\",\n \"in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx],\",\n \"nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set)\",\n \"e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional\",\n \"__rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self,\",\n \"`None` to use the default graph. Raises ------ ValueError If no `Graph` instance\",\n \"Real import contextlib import traceback import uuid import numpy as np import importlib\",\n \"return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def\",\n \"this node. If graph is `None`, this is the top-level graph. op_name :\",\n \"Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ)\",\n \"avoid the context being modified. Parameters ---------- fetches : list[str or Node] or\",\n \"this function returns a hash of all attributes and subgraphs of a node.\",\n \"other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node):\",\n \"else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val\",\n \"**kwargs): \\\"\\\"\\\" Evaluate one or more nodes given a dictionary of node names\",\n \"# Get a list of all dependencies relevant to this node self.dependencies =\",\n \"{self.args}\\\") if self.graph: graph = self.graph if self._name is not None and self._name\",\n \"= self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and c.op_name\",\n \"= nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ =\",\n \"list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes\",\n \"= nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ =\",\n \"`fetches` is not an `Node` instance, node name, or a sequence thereof. \\\"\\\"\\\"\",\n \"isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and\",\n \"\\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial\",\n \"slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def\",\n \"np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1,\",\n \"return node except Exception as ex: # pragma: no cover messages = []\",\n \"floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index\",\n \"[\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count >\",\n \"# pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context: raise\",\n \"= slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return\",\n \"self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance\",\n \"the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args,\",\n \"= nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ =\",\n \"to enable \\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE else: shapes =\",\n \"if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self]\",\n \"this node self.dependencies = [] if dependencies is None else dependencies if self.graph:\",\n \"cannot be found in the associated graph. \\\"\\\"\\\" # TODO: Need a way\",\n \"\\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self,\",\n \"nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview)\",\n \"map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_\",\n \"[] self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] #\",\n \"self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\"\",\n \"= nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ =\",\n \"else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not\",\n \"is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint\",\n \"evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages))\",\n \"None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node\",\n \"for a node. This can be a tuple of integers or parameter node\",\n \"object id, whereas this function returns a hash of all attributes and subgraphs\",\n \"__setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of the node\",\n \"if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif\",\n \"in context: if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name\",\n \"def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa)\",\n \"@staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by returning the\",\n \"if target is None: return functools.partial(nodeop, **kwargs) # This is called when the\",\n \"None Graph to return or `None` to use the default graph. Raises ------\",\n \"return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph)\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if\",\n \"`dependencies` are executed before any nodes in this scope. Parameters ---------- dependencies :\",\n \"mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_\",\n \"nodes. Parameters ---------- args : tuple Positional arguments passed to the `_evaluate` method.\",\n \": tuple or None Shape of the output for a node. This can\",\n \"if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else:\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other,\",\n \"name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed\",\n \"tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args)\",\n \"self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a\",\n \"not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape,\",\n \"isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder or integer value\",\n \"self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self,\",\n \"in range(num): yield self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self,\",\n \"self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add the node\",\n \"any internal stack traces fname = frame.filename if 'polymath' in fname: continue #\",\n \"nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod)\",\n \"an `Node` instance or an node name. RuntimeError If `node` is an `Node`\",\n \"name. Returns ------- values : Node or tuple[object] Output of the nodes given\",\n \"name of an node. Returns ------- instantiated_node : Node Node instance. Raises ------\",\n \"else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph)\",\n \"callback) if self in context: return context[self] # Evaluate the parents partial =\",\n \"else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other):\",\n \"nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter)\",\n \"neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift\",\n \"def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k:\",\n \"if key not in self.added_attrs} # Evaluate the node callback = callback or\",\n \"all explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return context[self] #\",\n \"Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in key:\",\n \"tuple of integers or parameter node names. graph : Node or None Parent\",\n \"{} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes =\",\n \"self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other):\",\n \"tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by returning\",\n \"graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s\",\n \"evaluated when an node is evaluated. Returns ------- value : object Output of\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other):\",\n \"> 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else: for idx,\",\n \"input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_\",\n \"other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other,\",\n \"passed to the target kwargs : dict keywoard arguments passed to the target\",\n \"\\\"\\\"\\\" # TODO: Need a way to check if the existing node is\",\n \"value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs)\",\n \"= Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph:\",\n \"slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not\",\n \"ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self,\",\n \"evaluate. context : dict or None Context in which to evaluate the nodes.\",\n \"Parameters ---------- graph : Node or None Graph to return or `None` to\",\n \"Evaluate the node given a context. Parameters ---------- context : dict Normalised context\",\n \"evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\"\",\n \"other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self,\",\n \"return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\"\",\n \"other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self)\",\n \"name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s\",\n \"callable function to evaluate the node args : tuple positional arguments passed to\",\n \"evaluating any nodes defined in this scope. \\\"\\\"\\\" # Add dependencies to the\",\n \"for nodes. Parameters ---------- args : tuple Positional arguments passed to the `_evaluate`\",\n \": cal The multi-dimensional variable used for indexing into. idx : tuple Tuple\",\n \": Node Node instance. Raises ------ ValueError If `node` is not an `Node`\",\n \"= nodeop(builtins.int) open_ = nodeop(builtins.open) str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ =\",\n \"len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\")\",\n \"def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor,\",\n \"if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def\",\n \"__hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional hash of a\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other,\",\n \"slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args\",\n \"\\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"= nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift =\",\n \"def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node names\",\n \"ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\"\",\n \"for key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the\",\n \"return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the\",\n \"return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__\",\n \"= c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0]\",\n \"of the node cannot be found in the associated graph. \\\"\\\"\\\" name =\",\n \"an object id, whereas this function returns a hash of all attributes and\",\n \"__or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other,\",\n \"= shape if isinstance(shape, tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set):\",\n \"op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else: arg0_dom\",\n \"Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other,\",\n \"def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"__repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def\",\n \"OrderedDict, Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real import\",\n \"other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self,\",\n \"ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph =\",\n \"fetches] return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self,\",\n \"already exists in the associated graph. KeyError If the current name of the\",\n \"object Output of the node given the context. \\\"\\\"\\\" # Evaluate all explicit\",\n \"node] if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if\",\n \"sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self,\",\n \"(int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if\",\n \"\\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph while g is not\",\n \"out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape)\",\n \"------ ValueError If `fetches` is not an `Node` instance, node name, or a\",\n \"def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred,\",\n \"else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"graph. Raises ------ ValueError If no `Graph` instance can be obtained. \\\"\\\"\\\" graph\",\n \"dependencies relevant to this node self.dependencies = [] if dependencies is None else\",\n \"!= len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name\",\n \"other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other):\",\n \": dict Normalised context in which to evaluate the node. callback : callable\",\n \"nodes by retrieving the node object associated with the node name. Parameters ----------\",\n \"ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and\",\n \"= nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open) str_ =\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not\",\n \"= tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args =\",\n \"element in node) if isinstance(node, list): return [partial(element) for element in node] if\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph)\",\n \"graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self,\",\n \"nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed)\",\n \"can be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self,\",\n \"and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other):\",\n \"or names. kwargs : dict[str, object] Additional context information keyed by variable name.\",\n \"Evaluate an node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try:\",\n \"active graph instance by returning the explicitly given graph or using the default\",\n \"not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context) #\",\n \"Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving\",\n \"use the default graph. Raises ------ ValueError If no `Graph` instance can be\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if\",\n \"target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"] def\",\n \"call when the node is executed. args : list Sequence of positional arguments\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return\",\n \"self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter\",\n \"the node name. Parameters ---------- node : Node or str Node instance or\",\n \"Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If `node` is\",\n \"or an node name. RuntimeError If `node` is an `Node` instance but does\",\n \"graph self._shape = OrderedDict() self.shape = shape or tuple([]) # Get a list\",\n \"= {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes\",\n \"bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_\",\n \"other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"\\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context,\",\n \"else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"self.added_attrs = [] # TODO: CHange this to underscore private variable self.kwargs =\",\n \"domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var\",\n \"if self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name)\",\n \"g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find\",\n \"nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint)\",\n \"or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches] context\",\n \"dependencies : list Sequence of nodes to be evaluted before evaluating any nodes\",\n \"in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name\",\n \"__ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\"\",\n \"n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args,\",\n \"other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self,\",\n \"= nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ =\",\n \"the last interactive cell if interactive and not fname.startswith('<'): break # pragma: no\",\n \"which to evaluate the nodes. callback : callable or None Callback to be\",\n \"other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other,\",\n \"= 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph)\",\n \"\\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of the\",\n \"\\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in\",\n \"the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5\",\n \"fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes given a\",\n \"Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies\",\n \"------- self : Node This node. Raises ------ ValueError If an node with\",\n \"super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_\",\n \"elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index):\",\n \"succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args:\",\n \"for n in node: stack = [] if isinstance(n, Node): for frame in\",\n \"self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name,\",\n \"target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\"\",\n \": Node or tuple[object] Output of the nodes given the context. Raises ------\",\n \"self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif hasattr(key,\",\n \"continue # Stop tracing at the last interactive cell if interactive and not\",\n \"other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other):\",\n \"= value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in\",\n \"slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var,\",\n \"and len(key) == 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if\",\n \"hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key):\",\n \"return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def\",\n \"as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in\",\n \"if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\"\",\n \"op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var\",\n \"shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x:\",\n \"is called when the decorator is used with arguments if target is None:\",\n \"isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node\",\n \"# pylint: disable=protected-access # Do not capture any internal stack traces fname =\",\n \"or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value =\",\n \"self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) ==\",\n \"other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other,\",\n \"method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes =\",\n \"slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var =\",\n \"positional arguments passed to the target kwargs : dict keywoard arguments passed to\",\n \"The default hash returns an object id, whereas this function returns a hash\",\n \"{self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys():\",\n \"args): new_args = [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__\",\n \"arguments passed to the target kwargs : dict keywoard arguments passed to the\",\n \"Base class for nodes. Parameters ---------- args : tuple Positional arguments passed to\",\n \"if interactive and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<')\",\n \"hash returns an object id, whereas this function returns a hash of all\",\n \"target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list,\",\n \"def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s,\",\n \"= f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self):\",\n \"{self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not\",\n \"other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other,\",\n \"graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`,\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph)\",\n \"slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def\",\n \"methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg\",\n \"of the node cannot be found in the associated graph. \\\"\\\"\\\" # TODO:\",\n \"dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph,\",\n \"= nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ =\",\n \"self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple): return\",\n \"dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return id(self)\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if\",\n \"ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is\",\n \"graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return\",\n \"(1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def\",\n \"__and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"if isinstance(node, Node) or not is_iterable(node): node = [node] for n in node:\",\n \"graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return\",\n \"if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k\",\n \"context.pop(node) node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value for\",\n \"ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None,\",\n \"= tuple([shape]) elif not shape or len(shape) == 0: # TODO: Change in\",\n \"return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif isinstance(key, tuple):\",\n \"for indexing into. idx : tuple Tuple of either integer values or index/index_op\",\n \"self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node))\",\n \"method. name : str or None Name of the node or `None` to\",\n \"def args(self, args): new_args = [] for arg in args: if isinstance(arg, Node):\",\n \"finalized\\\" to work self._shape = UNSET_SHAPE else: shapes = [] for dim in\",\n \"def shape(self): \\\"\\\"\\\" tuple : Shape of the output for a node. This\",\n \"!= self): raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph, instead belongs\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other,\",\n \"sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape =\",\n \"be found in the associated graph. \\\"\\\"\\\" # TODO: Need a way to\",\n \"indices which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\"\",\n \"self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self\",\n \"in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target\",\n \"to evaluate. context : dict or None Context in which to evaluate the\",\n \"axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape):\",\n \"the new name with a unique stringwhich corresponds to the variable, index combination.\",\n \"def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv,\",\n \"messages = [] interactive = False if isinstance(node, Node) or not is_iterable(node): node\",\n \": dict[Node, object] Normalized context whose keys are node instances. Raises ------ ValueError\",\n \": Parent graph of this node. If graph is `None`, this is the\",\n \"context information keyed by variable name. Returns ------- values : Node or tuple[object]\",\n \"self._args = [] self._predeecessors = [] self._succesors = [] self.args = args if\",\n \"op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x],\",\n \": Node or str Node instance or name of an node. Returns -------\",\n \"and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node =\",\n \"self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ ==\",\n \"\\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"except Exception as ex: # pragma: no cover messages = [] interactive =\",\n \"= _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if\",\n \"slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain =\",\n \"graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO: Need a way to\",\n \"self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and\",\n \"function to evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls,\",\n \"var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self,\",\n \"other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self,\",\n \"dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from\",\n \"If `node` is not an `Node` instance or an node name. RuntimeError If\",\n \"return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self,\",\n \"> 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for\",\n \"{indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res\",\n \"shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\")\",\n \"else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else\",\n \"context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node\",\n \"slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and\",\n \"or names to evaluate. context : dict or None Context in which to\",\n \"self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of the node and update\",\n \"node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if\",\n \"other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self)\",\n \"__xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"callback=None): \\\"\\\"\\\" Evaluate the node given a context. Parameters ---------- context : dict\",\n \"is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None):\",\n \"__pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return\",\n \"or None Context in which to evaluate the nodes. callback : callable or\",\n \": tuple positional arguments passed to the target kwargs : dict keywoard arguments\",\n \"or name of an node. Returns ------- instantiated_node : Node Node instance. Raises\",\n \"of positional arguments passed to `func`. kwargs : dict Mapping of keyword arguments\",\n \"= tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape =\",\n \"Integral) for sv in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0:\",\n \"__le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self,\",\n \"\\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self,\",\n \"shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x,\",\n \"def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_,\",\n \"graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to check for finalzied\",\n \"used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return\",\n \"Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var}\",\n \"name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes:\",\n \"graph. Parameters ---------- value : str Unique name of the node. Returns -------\",\n \"= nodeop(operator.ior) ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not =\",\n \"This returns the functional hash of a particular node. The default hash returns\",\n \"in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater than the variable\",\n \"assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain)\",\n \"in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args,\",\n \"---------- dependencies : list Sequence of nodes to be evaluted before evaluating any\",\n \"f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self): return\",\n \"tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape])\",\n \"in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property\",\n \"self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph:\",\n \"instantiated_node : Node Node instance. Raises ------ ValueError If `node` is not an\",\n \"self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ):\",\n \"and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d,\",\n \"a name for the node and add the node to the graph self._name\",\n \"return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def\",\n \"#pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ =\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph)\",\n \"not an `Node` instance or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate\",\n \"the node functionality. value : Any or None If a node has a\",\n \"f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name and self._name\",\n \"the decorator is used with arguments if target is None: return functools.partial(nodeop, **kwargs)\",\n \"return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def\",\n \"and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise\",\n \"raise ValueError(f\\\"Invalid shape var for var index {self} with variable shape {self.var.shape}\\\") return\",\n \"{list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name = [] if isinstance(key, Node):\",\n \"Name of the node or `None` to use a random, unique identifier. shape\",\n \"not None and name not in g.nodes: g = g.graph if name in\",\n \"indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True\",\n \"@property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray))\",\n \"def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for c\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph)\",\n \"Node or tuple[object] Output of the nodes given the context. Raises ------ ValueError\",\n \"_flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\"\",\n \"in this scope. \\\"\\\"\\\" # Add dependencies to the graph graph = Node.get_active_graph(graph)\",\n \"var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index\",\n \"slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def\",\n \"\\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for\",\n \"== 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index {self}\",\n \"__reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key,\",\n \"self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self,\",\n \"associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO: Need a way\",\n \"var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var,\",\n \"a graph by replacing all node names with node instances. .. note:: This\",\n \"Sequence of nodes to be evaluted before evaluating any nodes defined in this\",\n \"self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx,\",\n \"length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller\",\n \"is not equal to the current ndoe as ewll if self.graph and name\",\n \"else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = []\",\n \"returning the explicitly given graph or using the default graph. Parameters ---------- graph\",\n \"isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx\",\n \"control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed before any nodes in\",\n \"from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base\",\n \"this node and discard the values. Parameters ---------- context : dict Normalised context\",\n \"\\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other):\",\n \"else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs):\",\n \"nodes defined in this scope. \\\"\\\"\\\" # Add dependencies to the graph graph\",\n \"order to enable \\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE else: shapes\",\n \"self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True)\",\n \"**kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node): #\",\n \"print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_\",\n \"self._op_name = None self.op_name = op_name # Get the stack context so we\",\n \"node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for\",\n \"return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var\",\n \"for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] =\",\n \"float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self,\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph)\",\n \"self.name = name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get\",\n \"= Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"]\",\n \"f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val - 1) >= indices[-1][idx]\",\n \"else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None,\",\n \"nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter)\",\n \"or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235\",\n \"nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv)\",\n \"var def set_name(self, name): \\\"\\\"\\\" Set the name for a variable index, making\",\n \"Node wrapper for stateless functions. Parameters ---------- target : callable function to evaluate\",\n \"single = True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must\",\n \"all node names with node instances. .. note:: This function modifies the context\",\n \"tuple positional arguments passed to the target kwargs : dict keywoard arguments passed\",\n \"return ret def is_scalar(self, val=None): if val is not None and (not isinstance(val,\",\n \"[] # TODO: CHange this to underscore private variable self.kwargs = kwargs self.graph\",\n \"pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else\",\n \"self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)): var\",\n \"sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True\",\n \"else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1])\",\n \"and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self,\",\n \"constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name)\",\n \"reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal stack traces fname\",\n \"other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other,\",\n \"self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else\",\n \"{dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently\",\n \"of this node and discard the values. Parameters ---------- context : dict Normalised\",\n \"Raises ------ ValueError If no `Graph` instance can be obtained. \\\"\\\"\\\" graph =\",\n \"node. Raises ------ ValueError If an node with `value` already exists in the\",\n \"the node to the graph self._name = None self.name = name or uuid.uuid4().hex\",\n \"other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self)\",\n \"key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name +\",\n \"else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or\",\n \"nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem)\",\n \"isinstance(context, Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context) # Add\",\n \"len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i)\",\n \"graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return self def\",\n \"or None Callback to be evaluated when an node is evaluated. kwargs :\",\n \"other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self)\",\n \"matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne\",\n \"or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx,\",\n \"= [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names)))\",\n \"self._shape @property def var(self): return self @property def name(self): \\\"\\\"\\\"str : Unique name\",\n \"else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"\\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain\",\n \"name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph)\",\n \"graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s =\",\n \"nodes given a dictionary of node names with their values. .. note:: This\",\n \"= var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def\",\n \"and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var\",\n \"other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret =\",\n \"context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function to evaluate the node.\",\n \"other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self)\",\n \"[] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a\",\n \"= nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ =\",\n \"value must be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\")\",\n \"\\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"len(self) for i in range(num): yield self[i] def __eq__(self, other): return hash(self) ==\",\n \"graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not\",\n \"slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def\",\n \"= nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat =\",\n \"isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) ==\",\n \"dict keywoard arguments passed to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs):\",\n \"keyword arguments `kwargs`. Parameters ---------- func : callable Function to call when the\",\n \"@property def gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name) cgraph\",\n \"nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\"\",\n \"shape {self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices,\",\n \"\\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else\",\n \"ret def is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray)\",\n \"idx : tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def\",\n \"import contextlib import traceback import uuid import numpy as np import importlib from\",\n \"= list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys()))\",\n \"other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self)\",\n \"and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key)\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other):\",\n \"node. ValueError If `context` is not a mapping. \\\"\\\"\\\" if context is None:\",\n \"other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): #\",\n \"self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] =\",\n \"slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']]) return node except\",\n \"polymath import UNSET_SHAPE, DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping,\",\n \"graph = self.graph if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name,\",\n \"f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name is not\",\n \"fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property\",\n \"nodes should implement this function to evaluate the node. \\\"\\\"\\\" return self(*args, context,\",\n \"\\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in\",\n \"__rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"self.instantiate_graph(context, **kwargs) for c in context: if c in fetches and c.op_name in\",\n \"= [] self._predeecessors = [] self._succesors = [] self.args = args if \\\"name\\\"\",\n \"def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_,\",\n \"*args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value =\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self,\",\n \"of the nodes given the context. Raises ------ ValueError If `fetches` is not\",\n \"before any nodes in this scope. Parameters ---------- dependencies : list Sequence of\",\n \"nodes = list(context) # Add the keyword arguments for node in nodes: #\",\n \"thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches] context = self.instantiate_graph(context, **kwargs) for\",\n \"Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter\",\n \"self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name = name return\",\n \"nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains)\",\n \"self.graph while cgraph: scope_names.append(cgraph.name) cgraph = cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return\",\n \"---------- func : callable Function to call when the node is executed. args\",\n \"graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name, shape=shape,\",\n \"gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self,\",\n \"graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return\",\n \"can be a tuple of integers or parameter node names. graph : Node\",\n \"`Node` instance, node name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)):\",\n \"set_name(self, name): \\\"\\\"\\\" Set the name of the node and update the graph.\",\n \"return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if\",\n \"raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph, instead belongs to\\\" f\\\"\",\n \"shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif\",\n \"graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return\",\n \"other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other,\",\n \"# Do not capture any internal stack traces fname = frame.filename if 'polymath'\",\n \"len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i\",\n \"\\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self,\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other):\",\n \"Returns ------- values : Node or tuple[object] Output of the nodes given the\",\n \"KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if\",\n \"= nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg = nodeop(operator.neg) not_ =\",\n \"node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str : Operation name which describes\",\n \"stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" %\",\n \"graph. Parameters ---------- graph : Node or None Graph to return or `None`\",\n \"isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d)\",\n \"delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add\",\n \"be evaluated when an node is evaluated. Returns ------- value : object Output\",\n \"var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not None\",\n \"not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name]\",\n \"get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by returning the explicitly given\",\n \"\\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\"\",\n \"x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else\",\n \": dict Additional context information keyed by variable name. Returns ------- values :\",\n \"other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"names. graph : Node or None Parent graph of this node. If graph\",\n \"graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return\",\n \"to evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node,\",\n \"slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args,\",\n \"\\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node. If graph is `None`, this\",\n \"graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"= nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ =\",\n \"node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node in context:\",\n \"not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret\",\n \"self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or\",\n \"None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)):\",\n \"= var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape ==\",\n \"op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0],\",\n \"f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active\",\n \"= name or uuid.uuid4().hex # TODO: Need a way to check if the\",\n \"enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_\",\n \"instance, node name, or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node\",\n \"shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n = cls(name=name,\",\n \"other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if\",\n \"other.__rtruediv__(self) def __floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"\\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args\",\n \"self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def\",\n \"must be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape\",\n \"\\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"This node. Raises ------ ValueError If an node with `value` already exists in\",\n \"if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx =\",\n \"of the node given the context. \\\"\\\"\\\" # Evaluate all explicit dependencies first\",\n \"c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph)\",\n \"__ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" %\",\n \"__add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other,\",\n \"chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_\",\n \"dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else:\",\n \"mapping. \\\"\\\"\\\" if context is None: context = {} elif not isinstance(context, Mapping):\",\n \"tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\"\",\n \"== 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) ==\",\n \"tuple([]) # Get a list of all dependencies relevant to this node self.dependencies\",\n \"Node] or str or Node One or more `Node` instances or names to\",\n \"and cannot be sliced for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if\",\n \"describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if op_name:\",\n \"= list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res = var[indices]\",\n \"no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to\",\n \"@property def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name\",\n \"return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph)\",\n \"node instances. Raises ------ ValueError If the context specifies more than one value\",\n \"check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified\",\n \"Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for\",\n \"get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var, slice2_var\",\n \"ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\",\",\n \"node names with their values. .. note:: This function modifies the context in\",\n \":code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches : list[str or\",\n \"node cannot be found in the associated graph. \\\"\\\"\\\" name = name or\",\n \"= nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ =\",\n \"irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le\",\n \"= Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint:\",\n \"to underscore private variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict()\",\n \"slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([]))\",\n \"elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder or integer\",\n \"list or numpy array, and cannot be sliced for {self.name}\\\") elif isinstance(var, list):\",\n \"kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange this to underscore private variable self.kwargs\",\n \"in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise\",\n \"break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message\",\n \"var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,)\",\n \"use for execution, it can be set using `value`. kwargs : dict Keyword\",\n \"self._name @property def op_name(self): \\\"\\\"\\\" str : Operation name which describes the node\",\n \"shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape\",\n \"node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph while g is\",\n \"(slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key,\",\n \"else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self):\",\n \"@property def var(self): var, index_list = self.args return var def set_name(self, name): \\\"\\\"\\\"\",\n \"in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args =\",\n \"else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def\",\n \"isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda\",\n \"key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple):\",\n \"new name with a unique stringwhich corresponds to the variable, index combination. Parameters\",\n \"= _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else:\",\n \"arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\"\",\n \"any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_\",\n \"if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key:\",\n \"---------- value : str Unique name of the node. Returns ------- self :\",\n \"other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self)\",\n \"= nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ =\",\n \"slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\")\",\n \"= np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) >\",\n \"= node.evaluate(context) for name, value in kwargs.items(): node = self.nodes[name] if node in\",\n \"if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name\",\n \"other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self,\",\n \"if not single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val\",\n \"value : Any or None If a node has a default value to\",\n \"other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self)\",\n \"shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self):\",\n \"with node instances. .. note:: This function modifies the context in place. Use\",\n \"= tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph\",\n \"graph.nodes[name] = self self._name = name return self def __getitem__(self, key): if self.is_shape_finalized()\",\n \"name for the node and add the node to the graph self._name =\",\n \"values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return\",\n \"\\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper\",\n \"functions. \\\"\\\"\\\" # This is called when the decorator is used with arguments\",\n \"not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\"\",\n \"gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert\",\n \"variable name. Returns ------- values : Node or tuple[object] Output of the nodes\",\n \"(0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single =\",\n \"node in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c\",\n \"__bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the\",\n \"either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if\",\n \"unequal value for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\",\",\n \"graph. op_name : str Operation name which describes the node functionality. value :\",\n \"__rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self,\",\n \"other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if\",\n \":code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context : dict[Node or\",\n \"for node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if\",\n \"with type {type(var)} is not a list or numpy array, and cannot be\",\n \"fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return\",\n \"Any or None If a node has a default value to use for\",\n \"------ ValueError If the context specifies more than one value for any node.\",\n \"def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return\",\n \"(list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key,\",\n \"Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx\",\n \"{self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices =\",\n \"new_args = [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ ==\",\n \"cell if interactive and not fname.startswith('<'): break # pragma: no cover interactive =\",\n \"isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape,\",\n \"return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph)\",\n \"op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2) else:\",\n \"num = len(self) for i in range(num): yield self[i] def __eq__(self, other): return\",\n \"evaluate the nodes. callback : callable or None Callback to be evaluated when\",\n \"object] Additional context information keyed by variable name. Returns ------- normalized_context : dict[Node,\",\n \"_flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain\",\n \"other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self)\",\n \"name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph\",\n \"node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a context.\",\n \"\\\"\\\"\\\" Node representing multi-dimensional operations performed on a node. Parameters ---------- target :\",\n \"graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return\",\n \"self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield self[i] def\",\n \"unique identifier. shape : tuple or None Shape of the output for a\",\n \"var(self): var, index_list = self.args return var def set_name(self, name): \\\"\\\"\\\" Set the\",\n \"\\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape,\",\n \"= self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None\",\n \"f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node\",\n \"context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or\",\n \"or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self,\",\n \"of the output for a node. This can be a tuple of integers\",\n \"graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return\",\n \"compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_\",\n \"ValueError If `fetches` is not an `Node` instance, node name, or a sequence\",\n \"elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in key: if isinstance(k,\",\n \"node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop', 'step']])\",\n \"or None Name of the node or `None` to use a random, unique\",\n \"return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def\",\n \"\\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args\",\n \"f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise\",\n \"else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"_noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args,\",\n \"the name of the node and update the graph. Parameters ---------- value :\",\n \"not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None,\",\n \"str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph\",\n \"node : Node or str Node instance or name of an node. Returns\",\n \"(Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable\",\n \"other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self,\",\n \"**kwargs): if len(args) == 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value,\",\n \"this is the top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds def\",\n \"__rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other,\",\n \"def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\" # This\",\n \"= [] for arg in args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\":\",\n \"nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord)\",\n \"self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\"\",\n \"c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if\",\n \"of nodes to be evaluted before evaluating any nodes defined in this scope.\",\n \"slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def\",\n \"to be evaluted before evaluating any nodes defined in this scope. \\\"\\\"\\\" #\",\n \"instance but does not belong to this graph. \\\"\\\"\\\" if isinstance(node, str): return\",\n \"all dependencies relevant to this node self.dependencies = [] if dependencies is None\",\n \"graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k\",\n \"context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes given a dictionary\",\n \"= nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ =\",\n \"if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return\",\n \"graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self,\",\n \"**kwargs) self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if\",\n \"func : callable Function to call when the node is executed. args :\",\n \"a dictionary of node names with their values. .. note:: This function modifies\",\n \"= nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ =\",\n \"other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return\",\n \"the context. \\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if self\",\n \"= {key: partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs}\",\n \"key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None):\",\n \"var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert isinstance(shape,\",\n \"slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var,\",\n \"self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s\",\n \"Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103\",\n \"in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if not\",\n \"are used for executing this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return\",\n \"elif not shape or len(shape) == 0: # TODO: Change in order to\",\n \"`value` already exists in the associated graph. KeyError If the current name of\",\n \"context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node,\",\n \"def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return\",\n \"@contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are executed before any\",\n \"nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super)\",\n \"def args(self): \\\"\\\"\\\" tuple : Positional arguments which are used for executing this\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other):\",\n \"(slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx =\",\n \"self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self,\",\n \"= nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt =\",\n \"isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self)\",\n \"len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape} in\",\n \"self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx\",\n \">= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which\",\n \": callable function to evaluate the node args : tuple positional arguments passed\",\n \"graph or using the default graph. Parameters ---------- graph : Node or None\",\n \"for execution, it can be set using `value`. kwargs : dict Keyword arguments\",\n \"return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def\",\n \"**kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args\",\n \"graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return\",\n \"len(self.domain) == np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple)\",\n \"**kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op\",\n \"= nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ =\",\n \"indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res =\",\n \": Unique name of the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str\",\n \": str Operation name which describes the node functionality. value : Any or\",\n \"other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other):\",\n \"id, whereas this function returns a hash of all attributes and subgraphs of\",\n \"repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_\",\n \"self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def __repr__(self): return\",\n \"`None` to use a random, unique identifier. shape : tuple or None Shape\",\n \"index, making sure to replicate the new name with a unique stringwhich corresponds\",\n \"is executed. args : list Sequence of positional arguments passed to `func`. kwargs\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not\",\n \"k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name =\",\n \"= nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ =\",\n \"elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s)\",\n \"instance, node name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches\",\n \"in the associated graph. \\\"\\\"\\\" # TODO: Need a way to check if\",\n \"__enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self):\",\n \"functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if\",\n \"== 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n\",\n \"keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name = []\",\n \"nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_ = nodeop(builtins.oct)\",\n \"__mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices\",\n \"return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional\",\n \"class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint:\",\n \"all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args,\",\n \"def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"{self.name}\\\") elif isinstance(var, list): var = np.asarray(var) if len(var.shape) != len(out_shape) and np.prod(var.shape)\",\n \"= slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if\",\n \"to the target kwargs : dict keywoard arguments passed to the target \\\"\\\"\\\"\",\n \"target : cal The multi-dimensional variable used for indexing into. idx : tuple\",\n \"self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape or\",\n \"other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self):\",\n \"= slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"):\",\n \"with their values. .. note:: This function modifies the context in place. Use\",\n \"return self @property def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return\",\n \"Need a way to check if the existing node is not equal to\",\n \"but does not belong to this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node]\",\n \"def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul,\",\n \"value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None):\",\n \"self.shape = shape or tuple([]) # Get a list of all dependencies relevant\",\n \"\\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count > 0\",\n \"all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name}\",\n \"evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs):\",\n \"EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223\",\n \"self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape\",\n \"tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret =\",\n \"in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop',\",\n \"float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_\",\n \"= Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed\",\n \": tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other,\",\n \"of node names with their values. .. note:: This function modifies the context\",\n \"@property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args return\",\n \"to call when the node is executed. args : list Sequence of positional\",\n \"__rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"== np.product(shape) and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else\",\n \"kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx)\",\n \"{self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name}\",\n \"cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape,\",\n \"target is None: return functools.partial(nodeop, **kwargs) # This is called when the decorator\",\n \"found in the associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO:\",\n \"**kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with\",\n \"dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other,\",\n \"__rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self,\",\n \"the node. callback : callable or None Callback to be evaluated when an\",\n \"for a in self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape of the\",\n \"way to check if the existing node is not equal to the current\",\n \"def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function to\",\n \"None and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name =\",\n \"\\\"\\\"\\\" Node wrapper for stateless functions. Parameters ---------- target : callable function to\",\n \"raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph\",\n \"fname = frame.filename if 'polymath' in fname: continue # Stop tracing at the\",\n \"= nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ =\",\n \"Callback to be evaluated when an node is evaluated. \\\"\\\"\\\" for node in\",\n \"a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph while g\",\n \"= tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return ret else:\",\n \"if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([])\",\n \"{self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs):\",\n \"to avoid the context being modified. Parameters ---------- fetches : list[str or Node]\",\n \"returns the functional hash of a particular node. The default hash returns an\",\n \"g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph\",\n \"Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e,\",\n \"node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError):\",\n \"node. Parameters ---------- target : cal The multi-dimensional variable used for indexing into.\",\n \"dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for\",\n \"(not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape\",\n \"an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of\",\n \"a mapping. \\\"\\\"\\\" if context is None: context = {} elif not isinstance(context,\",\n \"slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var,\",\n \"other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self,\",\n \"name which describes the node functionality. value : Any or None If a\",\n \"node is evaluated. kwargs : dict Additional context information keyed by variable name.\",\n \"elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx,\",\n \"in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if\",\n \"self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] =\",\n \"If `fetches` is not an `Node` instance, node name, or a sequence thereof.\",\n \"return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def\",\n \"node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context)\",\n \"in args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg)\",\n \"the node and update the graph. Parameters ---------- value : str Unique name\",\n \"= [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single else\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if not\",\n \"self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other):\",\n \"= nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ =\",\n \"return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value\",\n \"if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes.\",\n \"check if the existing node is not equal to the current ndoe as\",\n \"nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf)\",\n \"@graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name]\",\n \"cgraph.graph return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self\",\n \"= [] if dependencies is None else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose\",\n \"isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var,\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not\",\n \"tuple[object] Output of the nodes given the context. Raises ------ ValueError If `fetches`\",\n \"nodes in this scope. Parameters ---------- dependencies : list Sequence of nodes to\",\n \"graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other): return\",\n \": Node or None Parent graph of this node. If graph is `None`,\",\n \"return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional hash of a particular\",\n \"{self}') return self.shape[0] def __iter__(self): num = len(self) for i in range(num): yield\",\n \"dict[Node or str, object] Context whose keys are node instances or names. kwargs\",\n \"return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k])\",\n \"self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other):\",\n \"used for executing this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name\",\n \"{self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if\",\n \"[partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value) for key,\",\n \"nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass)\",\n \"------- normalized_context : dict[Node, object] Normalized context whose keys are node instances. Raises\",\n \"__rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args,\",\n \"1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var for var index {self} with\",\n \"not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self,\",\n \"interactive cell if interactive and not fname.startswith('<'): break # pragma: no cover interactive\",\n \"of the node. Returns ------- self : Node This node. Raises ------ ValueError\",\n \"slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def\",\n \"key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if\",\n \"return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph)\",\n \"value to use for execution, it can be set using `value`. kwargs :\",\n \"list of all dependencies relevant to this node self.dependencies = [] if dependencies\",\n \"pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object associated with the node\",\n \"nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes)\",\n \"self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def\",\n \"def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph()\",\n \"functionality. value : Any or None If a node has a default value\",\n \"float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node):\",\n \"belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an `Node` instance\",\n \"Output of the nodes given the context. Raises ------ ValueError If `fetches` is\",\n \"\\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self,\",\n \"def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv,\",\n \"else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return\",\n \"nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round)\",\n \"== Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value):\",\n \"ValueError(\\\"`fetches` must be an `Node` instance, node name, or a \\\" \\\"sequence thereof.\\\")\",\n \"context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args,\",\n \"be a mapping.\\\") nodes = list(context) # Add the keyword arguments for node\",\n \"name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value\",\n \"__rfloordiv__(self, other): return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2\",\n \"complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_\",\n \"= var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else:\",\n \"{var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist()\",\n \"or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self,\",\n \"1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1)\",\n \"= cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self):\",\n \"the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\"\",\n \"node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack): # pylint:\",\n \"\\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def\",\n \"dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager\",\n \"= self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom)\",\n \"self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other):\",\n \"key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\"\",\n \"not single and not all([(idx_val - 1) >= indices[-1][idx] for idx, idx_val in\",\n \"= list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return\",\n \"def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_,\",\n \"`_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes\",\n \"= name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of\",\n \"graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint:\",\n \"graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name]\",\n \"this to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must\",\n \"node and update the graph. Parameters ---------- value : str Unique name of\",\n \"name or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack\",\n \"key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key],\",\n \"slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def _evaluate(self, *args, **kwargs): for\",\n \"other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self)\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return\",\n \"ValueError If `context` is not a mapping. \\\"\\\"\\\" if context is None: context\",\n \"return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def\",\n \"elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name,\",\n \"return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name)\",\n \"vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_\",\n \"in which to evaluate the nodes. callback : callable or None Callback to\",\n \"for fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self): return\",\n \"evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values\",\n \"raise ValueError(f\\\"var_index {self.name} has indices which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs:\",\n \"% (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless\",\n \"self.args = args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO:\",\n \"is the top-level graph. op_name : str Operation name which describes the node\",\n \"slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def\",\n \"this function to evaluate the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def\",\n \"= nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ =\",\n \"other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other):\",\n \"[fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single else tuple(values)\",\n \"= self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\")\",\n \"values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0] if single\",\n \"self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def\",\n \"Evaluate the dependencies of this node and discard the values. Parameters ---------- context\",\n \"graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return\",\n \"If a node has a default value to use for execution, it can\",\n \"= self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name) elif\",\n \"kwargs : dict Additional context information keyed by variable name. Returns ------- values\",\n \"def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub,\",\n \"the dependencies of this node and discard the values. Parameters ---------- context :\",\n \"def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph\",\n \"def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"TODO: Change in order to enable \\\"is shape finalized\\\" to work self._shape =\",\n \"= nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ =\",\n \"self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does not belong to {self}\",\n \"return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph)\",\n \"self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other):\",\n \"in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values =\",\n \"TODO: Need a way to check if the existing node is not equal\",\n \"self in context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context,\",\n \"return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def\",\n \"current name of the node cannot be found in the associated graph. \\\"\\\"\\\"\",\n \"if isinstance(var, (Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)):\",\n \"self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name =\",\n \"*args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func`\",\n \"**kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters\",\n \"def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return self._name @property def\",\n \"= nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ =\",\n \"and discard the values. Parameters ---------- context : dict Normalised context in which\",\n \"= args if \\\"name\\\" in kwargs: kwargs.pop(\\\"name\\\") self.added_attrs = [] # TODO: CHange\",\n \"the default graph. Parameters ---------- graph : Node or None Graph to return\",\n \"an node is evaluated. kwargs : dict Additional context information keyed by variable\",\n \"return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph)\",\n \"node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value if\",\n \"self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self):\",\n \"arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function to\",\n \"and len(shape) > 0: self._shape = shape if isinstance(shape, tuple) else tuple(shape) else:\",\n \"\\\"\\\"\\\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield #\",\n \"issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_\",\n \"return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is\",\n \"fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c in fetches\",\n \"reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_\",\n \"abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def\",\n \"is None and slice2_var is None: slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op,\",\n \"else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) ==\",\n \"for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in\",\n \"slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def\",\n \"@name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for\",\n \"return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def\",\n \"name of the node cannot be found in the associated graph. \\\"\\\"\\\" name\",\n \"True else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var):\",\n \"nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input)\",\n \"the node or `None` to use a random, unique identifier. shape : tuple\",\n \"to the variable, index combination. Parameters ---------- value : str Unique name of\",\n \"= self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)):\",\n \"= None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\",\",\n \"return var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other,\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if\",\n \"self.shape == UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral):\",\n \"= True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must be\",\n \"_is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index))\",\n \"else dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and\",\n \"else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n\",\n \"True elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must be an\",\n \"add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self,\",\n \"Node) else a for a in self.args] @property def shape(self): \\\"\\\"\\\" tuple :\",\n \"res = var[indices] if single else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape\",\n \"= functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node)\",\n \"{node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\")\",\n \"None If a node has a default value to use for execution, it\",\n \"arguments passed to the `_evaluate` method. name : str or None Name of\",\n \"\\\"\\\"\\\" Ensure that all `dependencies` are executed before any nodes in this scope.\",\n \"1) def _evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value =\",\n \"be a tuple of integers or parameter node names. \\\"\\\"\\\" return self._shape @property\",\n \"xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self,\",\n \"kwargs : dict[str, object] Additional context information keyed by variable name. Returns -------\",\n \"context : dict Normalised context in which to evaluate the node. callback :\",\n \"str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with\",\n \"value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies = [] self._args =\",\n \"If the context specifies more than one value for any node. ValueError If\",\n \"'step']]) return node except Exception as ex: # pragma: no cover messages =\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not\",\n \"to be evaluated when an node is evaluated. Returns ------- value : object\",\n \"graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from the graph del\",\n \"= nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor =\",\n \"instance can be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph def\",\n \"by replacing all node names with node instances. .. note:: This function modifies\",\n \"other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"instead belongs to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an `Node`\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph)\",\n \"nodeop(builtins.set) add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat)\",\n \"raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return\",\n \"instances. Raises ------ ValueError If the context specifies more than one value for\",\n \"Exception as ex: # pragma: no cover messages = [] interactive = False\",\n \"dependencies if self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and add\",\n \"__rand__(self, other): return and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"for i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else:\",\n \"return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for element\",\n \"can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self):\",\n \"args(self, args): new_args = [] for arg in args: if isinstance(arg, Node): if\",\n \"stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None,\",\n \"__rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self,\",\n \"def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies,\",\n \"is_scalar(self, val=None): if val is not None and (not isinstance(val, np.ndarray) or (len(val.shape)\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other,\",\n \"DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices =\",\n \"zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_\",\n \"is None: context = {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be\",\n \"ex: # pragma: no cover messages = [] interactive = False if isinstance(node,\",\n \"staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_\",\n \"= Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target\",\n \"self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\"\",\n \"which describes the node functionality. value : Any or None If a node\",\n \"self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else:\",\n \"Change in order to enable \\\"is shape finalized\\\" to work self._shape = UNSET_SHAPE\",\n \"------ ValueError If no `Graph` instance can be obtained. \\\"\\\"\\\" graph = graph\",\n \"nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id)\",\n \"functional hash of a particular node. The default hash returns an object id,\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other):\",\n \"type {type(var)} is not a list or numpy array, and cannot be sliced\",\n \"_wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args`\",\n \"self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other,\",\n \"[a.name if isinstance(a, Node) else a for a in self.args] @property def shape(self):\",\n \"other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx,\",\n \"context: if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name =\",\n \"if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys:\",\n \"using `value`. kwargs : dict Keyword arguments passed to the `_evaluate` method. \\\"\\\"\\\"\",\n \"= f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def\",\n \"default value to use for execution, it can be set using `value`. kwargs\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other):\",\n \"raise ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\") def instantiate_graph(self, context,\",\n \"defined in this scope. \\\"\\\"\\\" # Add dependencies to the graph graph =\",\n \"# This is called when the decorator is used without arguments @functools.wraps(target) def\",\n \"functools from numbers import Integral, Rational, Real import contextlib import traceback import uuid\",\n \"def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_,\",\n \"try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs)\",\n \"node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\"\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph)\",\n \"self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx =\",\n \"node name. Parameters ---------- node : Node or str Node instance or name\",\n \"self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node):\",\n \"self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an node. \\\"\\\"\\\" class var_index(Node):\",\n \"to use for execution, it can be set using `value`. kwargs : dict\",\n \"fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack)))\",\n \"__getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) == 0: return self\",\n \"= nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ =\",\n \"nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list)\",\n \"self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph != self):\",\n \"the node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph :\",\n \"self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self,\",\n \"object] Context whose keys are node instances or names. kwargs : dict[str, object]\",\n \"hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__\",\n \"explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self) for i\",\n \"Obtain the currently active graph instance by returning the explicitly given graph or\",\n \"node to the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return add(other, self,\",\n \"node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context,\",\n \"currently active graph instance by returning the explicitly given graph or using the\",\n \"to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from\",\n \"return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph)\",\n \"execution, it can be set using `value`. kwargs : dict Keyword arguments passed\",\n \"**kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs):\",\n \"which to evaluate the node. callback : callable or None Callback to be\",\n \"np import importlib from .graph import Graph from .domain import Domain from .util\",\n \"isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([]))\",\n \"ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter\",\n \"**kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs)\",\n \"# pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable corresponding to input\",\n \"\\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items(): node\",\n \"Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder\",\n \"------- instantiated_node : Node Node instance. Raises ------ ValueError If `node` is not\",\n \"and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New:\",\n \"name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0: n =\",\n \"return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def\",\n \"indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape,\",\n \"nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ = nodeop(builtins.open)\",\n \"given a dictionary of node names with their values. .. note:: This function\",\n \"else: graph.nodes[name] = self self._name = name return self def evaluate_dependencies(self, context, callback=None):\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other):\",\n \"var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret\",\n \"filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_\",\n \"= deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0 def __init__(self,\",\n \"{self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name and\",\n \"be specified explicitly for nodes {self}') return self.shape[0] def __iter__(self): num = len(self)\",\n \"new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self,\",\n \"var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if isinstance(slice2_var,\",\n \"__rlshift__(self, other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"Integral): return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape\",\n \"with the node name. Parameters ---------- node : Node or str Node instance\",\n \"nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt)\",\n \"Evaluate the node callback = callback or _noop_callback with callback(self, context): if self.__class__.__name__\",\n \"inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def\",\n \"set_shape(self, shape=None, init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral)\",\n \"Parent graph of this node. If graph is `None`, this is the top-level\",\n \"**kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"],\",\n \"setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor\",\n \"[partial(arg) for arg in self.args] kwargs = {key: partial(value) for key, value in\",\n \"**kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function to evaluate the node. \\\"\\\"\\\"\",\n \"else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self)\",\n \"def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) ==\",\n \"Evaluate one or more nodes given a dictionary of node names with their\",\n \"the current ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate\",\n \"other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self)\",\n \"def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self,\",\n \"if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx\",\n \"**kwargs) # This is called when the decorator is used without arguments @functools.wraps(target)\",\n \"if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx\",\n \"= slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self,\",\n \"other.__ne__(self) def __gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"arguments passed to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] =\",\n \"evaluted before evaluating any nodes defined in this scope. \\\"\\\"\\\" # Add dependencies\",\n \"if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var\",\n \"self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for\",\n \"graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key],\",\n \"called when the decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return\",\n \"nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in context:\",\n \"graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index\",\n \"else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain =\",\n \"return False return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape =\",\n \"slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def\",\n \"var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain\",\n \"self, graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other):\",\n \"op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name\",\n \"to this node self.dependencies = [] if dependencies is None else dependencies if\",\n \"== 0: # TODO: Change in order to enable \\\"is shape finalized\\\" to\",\n \"self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def\",\n \"scope. \\\"\\\"\\\" # Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield\",\n \"nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr)\",\n \"or parameter node names. graph : Node or None Parent graph of this\",\n \"graph self._name = None self.name = name or uuid.uuid4().hex self._op_name = None self.op_name\",\n \"if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid\",\n \"elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0],\",\n \"return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not\",\n \"**kwargs) else: context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args,\",\n \"all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_\",\n \"None self.op_name = op_name # Get the stack context so we can report\",\n \"Parameters ---------- context : dict Normalised context in which to evaluate the node.\",\n \"find {name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this to\",\n \"the currently active graph instance by returning the explicitly given graph or using\",\n \"graph=self.graph) def __floordiv__(self, other): return slice_op(operator.floordiv, self, other, graph=self.graph) def __rfloordiv__(self, other): return\",\n \"belong to this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node):\",\n \"\\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\"))\",\n \"decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner,\",\n \"len(shape) == 0: # TODO: Change in order to enable \\\"is shape finalized\\\"\",\n \"\\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self == Node._graph_stack.pop()\",\n \"to avoid the context being modified. Parameters ---------- context : dict[Node or str,\",\n \"super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def\",\n \"evaluated when an node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback)\",\n \"self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting\",\n \"= [] # TODO: CHange this to underscore private variable self.kwargs = kwargs\",\n \"name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this\",\n \"isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else:\",\n \"arguments if target is None: return functools.partial(nodeop, **kwargs) # This is called when\",\n \"isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self)\",\n \"value def is_shape_finalized(self): if self.shape == UNSET_SHAPE: return False for s in self.shape:\",\n \"self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"] = f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def\",\n \"or None Callback to be evaluated when an node is evaluated. \\\"\\\"\\\" for\",\n \"value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback\",\n \"= nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos =\",\n \"self self._name = name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the\",\n \"def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain:\",\n \"instances or names to evaluate. context : dict or None Context in which\",\n \"isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self,\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other, self,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self,\",\n \"idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater than\",\n \"a mapping.\\\") nodes = list(context) # Add the keyword arguments for node in\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return\",\n \"or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape = tuple(shapes) @staticmethod\",\n \"instance or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by\",\n \"and subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g =\",\n \"items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating\",\n \"Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret = self.nodes.item_by_index(idx) return\",\n \"__ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self,\",\n \"\\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"op_name : str Operation name which describes the node functionality. value : Any\",\n \"Ensure that all `dependencies` are executed before any nodes in this scope. Parameters\",\n \"elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else:\",\n \"not in self.added_attrs} # Evaluate the node callback = callback or _noop_callback with\",\n \"self.nodes = Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors\",\n \"Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance,\",\n \"(node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class\",\n \"context. Raises ------ ValueError If `fetches` is not an `Node` instance, node name,\",\n \"one value for any node. ValueError If `context` is not a mapping. \\\"\\\"\\\"\",\n \"s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in\",\n \"slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add,\",\n \"for frame in reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal\",\n \"\\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"add = nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains\",\n \"the graph self._name = None self.name = name or uuid.uuid4().hex self._op_name = None\",\n \"value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node]\",\n \"other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self)\",\n \"= nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt) index =\",\n \"context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not\",\n \"\\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if not _is_node_type_instance(other,\",\n \"other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self)\",\n \"tuple([key]), graph=self) return ret def is_scalar(self, val=None): if val is not None and\",\n \"integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): #\",\n \"Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs\",\n \"an node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self,\",\n \"def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional hash of\",\n \"Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple :\",\n \"from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph)\",\n \"return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph)\",\n \"graph=self.graph) def __xor__(self, other): return slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return\",\n \"= nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ =\",\n \"variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds:\",\n \"graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\"\",\n \"the associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO: Need a\",\n \"if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence):\",\n \"it can be set using `value`. kwargs : dict Keyword arguments passed to\",\n \"def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape\",\n \"to evaluate the nodes. callback : callable or None Callback to be evaluated\",\n \"graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret def is_scalar(self, val=None): if\",\n \"is not a list or numpy array, and cannot be sliced for {self.name}\\\")\",\n \"associated with the node name. Parameters ---------- node : Node or str Node\",\n \"slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_\",\n \".domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class\",\n \"def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg\",\n \"op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name =\",\n \"tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var,\",\n \"a list of all dependencies relevant to this node self.dependencies = [] if\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other):\",\n \"if op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else:\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return mod(other, self, graph=self.graph) if\",\n \"the node args : tuple positional arguments passed to the target kwargs :\",\n \"self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self,\",\n \"with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func : callable\",\n \"is evaluated. kwargs : dict Additional context information keyed by variable name. Returns\",\n \"in which to evaluate the node. callback : callable or None Callback to\",\n \"else: if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self,\",\n \"= graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate\",\n \"op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return\",\n \"2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain =\",\n \"TypeError(f\\\"Shape value must be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs}\",\n \"= self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([])\",\n \"self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is\",\n \"= self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] =\",\n \"self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif _is_node_type_instance(slice1_var,\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self, other): return mul(other, self, graph=self.graph) if\",\n \"name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in key: if\",\n \"Normalised context in which to evaluate the node. callback : callable or None\",\n \"self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other):\",\n \"domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list = self.args return var def\",\n \"def call(func, *args, **kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and keyword\",\n \"Node or None Graph to return or `None` to use the default graph.\",\n \": Shape of the output for a node. This can be a tuple\",\n \"self._shape = UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim,\",\n \"dict[str, object] Additional context information keyed by variable name. Returns ------- normalized_context :\",\n \"to check if the existing node is not equal to the current ndoe\",\n \"dict or None Context in which to evaluate the nodes. callback : callable\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not\",\n \"var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable corresponding to\",\n \"sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ = nodeop(builtins.input) oct_\",\n \"g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO:\",\n \"and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if\",\n \"isinstance(node, Node): if node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node\",\n \"= value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized():\",\n \"other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self,\",\n \"= g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in\",\n \"def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args))\",\n \"= nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ =\",\n \"in self.added_attrs} # Evaluate the node callback = callback or _noop_callback with callback(self,\",\n \"one or more nodes given a dictionary of node names with their values.\",\n \"other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other):\",\n \"ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_\",\n \"return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return pos(self, graph=self.graph)\",\n \"nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ = nodeop(builtins.hash)\",\n \"else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret\",\n \"Additional context information keyed by variable name. Returns ------- normalized_context : dict[Node, object]\",\n \"= nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior =\",\n \"name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list\",\n \"idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater than the\",\n \"wrapper for stateless functions. Parameters ---------- target : callable function to evaluate the\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other, graph=self.graph) if\",\n \"{var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\"\",\n \"Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\" + \\\"][\\\".join(name)\",\n \"__ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"+ \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self,\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if\",\n \"used for indexing into. idx : tuple Tuple of either integer values or\",\n \"= Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else:\",\n \"nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions. \\\"\\\"\\\" # This is\",\n \"= nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ =\",\n \"nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted)\",\n \"\\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg) for arg in self.args] kwargs = {key:\",\n \"all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx)\",\n \"isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice):\",\n \"if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)): return\",\n \"nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul)\",\n \"in node) if isinstance(node, list): return [partial(element) for element in node] if isinstance(node,\",\n \"self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key,\",\n \"in kwargs \\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"DEFAULT_SHAPES import builtins import operator from collections import OrderedDict, Mapping, Sequence, deque import\",\n \"`None`, this is the top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds\",\n \"= np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self,\",\n \"so we can report where the node was defined self._stack = traceback.extract_stack(limit=1) @property\",\n \"= tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not shape or len(shape)\",\n \"in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res = res[0]\",\n \"s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape)\",\n \"else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"def __getitem__(self, key): return self @property def domain(self): return self.kwargs[\\\"domain\\\"] def get_index_nodes(self, slice1_var=None,\",\n \"\\\\ else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list)\",\n \"nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex)\",\n \"shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif\",\n \"self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this\",\n \"isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx,\",\n \"def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return\",\n \"was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph\",\n \"nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple)\",\n \"domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target =\",\n \"le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul\",\n \"return [a.name if isinstance(a, Node) else a for a in self.args] @property def\",\n \"of all dependencies relevant to this node self.dependencies = [] if dependencies is\",\n \"callable or None Callback to be evaluated when an node is evaluated. kwargs\",\n \"{partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node, attr))\",\n \"__sub__(self, other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\"\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__radd__(self) def __radd__(self, other): return\",\n \"of integers or parameter node names. graph : Node or None Parent graph\",\n \"to the current ndoe as ewll if self.graph and name in self.graph.nodes: raise\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not\",\n \"other.__lt__(self) def __ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self,\",\n \"which are greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if\",\n \"= nodeop(builtins.delattr) hash_ = nodeop(builtins.hash) memoryview_ = nodeop(builtins.memoryview) set_ = nodeop(builtins.set) add =\",\n \"\\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies`\",\n \"callable or None Callback to be evaluated when an node is evaluated. \\\"\\\"\\\"\",\n \"nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt = nodeop(operator.gt)\",\n \"graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def __lt__(self, other): return\",\n \"other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other,\",\n \"graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return\",\n \"or Node One or more `Node` instances or names to evaluate. context :\",\n \"var index {self} with variable shape {self.var.shape}\\\") return True else: return self.var.shape ==\",\n \"(np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not a list or\",\n \"other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other,\",\n \"import operator from collections import OrderedDict, Mapping, Sequence, deque import functools from numbers\",\n \"else kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx\",\n \"def __rxor__(self, other): return xor(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"**kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other,\",\n \"modified. Parameters ---------- fetches : list[str or Node] or str or Node One\",\n \"for executing this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name if\",\n \"context[self] = self.value = self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs):\",\n \"else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"list Sequence of nodes to be evaluted before evaluating any nodes defined in\",\n \"nodes to be evaluted before evaluating any nodes defined in this scope. \\\"\\\"\\\"\",\n \"node instances or names. kwargs : dict[str, object] Additional context information keyed by\",\n \"other): return lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self)\",\n \"var, index_list = self.args return var def set_name(self, name): \\\"\\\"\\\" Set the name\",\n \"indices) return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self,\",\n \"node in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node\",\n \"in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value if node.op_name\",\n \"given the context. \\\"\\\"\\\" # Evaluate all explicit dependencies first self.evaluate_dependencies(context, callback) if\",\n \"def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set\",\n \"\\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def\",\n \": callable Function to call when the node is executed. args : list\",\n \"= self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter\",\n \"op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def\",\n \"return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph)\",\n \"interactive and not fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame)\",\n \"`func`. kwargs : dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return\",\n \"or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape != DEFAULT_SHAPES[0] and\",\n \"nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex) hasattr_ = nodeop(builtins.hasattr)\",\n \"has a default value to use for execution, it can be set using\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other):\",\n \"def var(self): return self @property def name(self): \\\"\\\"\\\"str : Unique name of the\",\n \"other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self)\",\n \"res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self,\",\n \"kwargs.pop(\\\"domain\\\") elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx =\",\n \"tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var,\",\n \"cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return\",\n \"of a particular node. The default hash returns an object id, whereas this\",\n \"add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in\",\n \"*args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else f\\\"{target.__name__}\\\" if\",\n \"__pos__(self): return pos(self, graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key):\",\n \"is_iterable(node): node = [node] for n in node: stack = [] if isinstance(n,\",\n \"other, self, graph=self.graph) def __lt__(self, other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self,\",\n \"in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for\",\n \"f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\")\",\n \"__rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self,\",\n \"obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): #\",\n \"combination. Parameters ---------- value : str Unique name of the node. Returns -------\",\n \"def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name,\",\n \"UNSET_SHAPE else: shapes = [] for dim in shape: if isinstance(dim, (Node, Integral)):\",\n \"if slice1_var is None and slice2_var is None: slice1_var, slice2_var = self.args if\",\n \"as ex: # pragma: no cover messages = [] interactive = False if\",\n \"str Operation name which describes the node functionality. value : Any or None\",\n \"isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def\",\n \"other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self)\",\n \"raise ValueError(f\\\"Index list does not match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for\",\n \"other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self,\",\n \"for name, value in kwargs.items(): node = self.nodes[name] if node in context: raise\",\n \"names. kwargs : dict[str, object] Additional context information keyed by variable name. Returns\",\n \"(node.graph != self): raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph, instead\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other):\",\n \"in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context\",\n \"_wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args,\",\n \"False else: raise ValueError(\\\"`fetches` must be an `Node` instance, node name, or a\",\n \"arguments `kwargs`. Parameters ---------- func : callable Function to call when the node\",\n \"other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an `Node` instance or node\",\n \"= nodeop(operator.add) and_ = nodeop(operator.and_) attrgetter = nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains =\",\n \"else: graph.nodes[name] = self self._name = name return self def __getitem__(self, key): if\",\n \"return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies` are\",\n \"KeyError(f\\\"{name} not in {self.name} keys:\\\\n\\\" f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret\",\n \"isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return not\",\n \"np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1:\",\n \"\\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO: Need a way to check\",\n \"Returns ------- self : Node This node. Raises ------ ValueError If an node\",\n \"== 1)): if self.var.shape != DEFAULT_SHAPES[0] and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)):\",\n \"var = np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type\",\n \"or tuple[object] Output of the nodes given the context. Raises ------ ValueError If\",\n \"_noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes.\",\n \"with a unique stringwhich corresponds to the variable, index combination. Parameters ---------- value\",\n \"if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not\",\n \"shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder or\",\n \"`Graph` instance can be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other):\",\n \"self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items() if key not\",\n \"= nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ =\",\n \"nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self, other): return or_(other, self, graph=self.graph) if not\",\n \"\\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs):\",\n \"len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1])\",\n \"being modified. Parameters ---------- context : dict[Node or str, object] Context whose keys\",\n \"def func_hash(self): \\\"\\\"\\\" This returns the functional hash of a particular node. The\",\n \"elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val): return\",\n \"self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node\",\n \"sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self,\",\n \"**kwargs) return n def __bool__(self): return True def __hash__(self): return id(self) def func_hash(self):\",\n \"which are used for executing this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self):\",\n \"context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs)\",\n \"\\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations\",\n \"elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def\",\n \"aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self,\",\n \"self, other, graph=self.graph) def __repr__(self): return \\\"<slice_%s '%s'>\\\" % (self.target.__name__, self.name) class func_op(Node):\",\n \"self, other, graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self,\",\n \"args = [partial(arg) for arg in self.args] kwargs = {key: partial(value) for key,\",\n \"slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other):\",\n \"matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_\",\n \"kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def\",\n \"Mapping, Sequence, deque import functools from numbers import Integral, Rational, Real import contextlib\",\n \"self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name = self.__class__.__name__ @name.setter def name(self,\",\n \"an `Node` instance or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a\",\n \"if the existing node is not equal to the current ndoe as ewll\",\n \"return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph)\",\n \"func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def call(func, *args, **kwargs): \\\"\\\"\\\" Call\",\n \"value=value, **kwargs) return n def __bool__(self): return True def __hash__(self): return id(self) def\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return\",\n \"shape and isinstance(shape[idx], float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain)\",\n \"eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem = nodeop(operator.getitem) gt\",\n \"of the node or `None` to use a random, unique identifier. shape :\",\n \"kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self, *args, **kwargs) def\",\n \"= nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg =\",\n \"to check for finalzied shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def __sub__(self, other): return sub(self,\",\n \"> 0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape):\",\n \"(Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be\",\n \"run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes given\",\n \"self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] =\",\n \"= nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ =\",\n \"self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self): return self.kwargs[\\\"domain\\\"]\",\n \"self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in args: if\",\n \"else other.__mod__(self) def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"\\\"\\\"\\\" str : Operation name which describes the node functionality. \\\"\\\"\\\" return self._op_name\",\n \"def __lshift__(self, other): return lshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name =\",\n \"(slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return\",\n \"itemgetter = nodeop(operator.itemgetter) le = nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt\",\n \"Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to\",\n \"node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs)\",\n \"= nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ =\",\n \"return True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif\",\n \"__truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other,\",\n \"self._graph def preds(self): return self._preds def succs(self): return self._preds def add_predecessor(self, pred): if\",\n \"pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self,\",\n \"if isinstance(x, np.ndarray) else x, indices)) res = var[indices] if single else np.asarray([var[idx]\",\n \"multi-dimensional operations performed on a node. Parameters ---------- target : cal The multi-dimensional\",\n \"= self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain elif\",\n \"graph. \\\"\\\"\\\" return self._graph def preds(self): return self._preds def succs(self): return self._preds def\",\n \"other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self,\",\n \"{self.domain}\\\\n\\\" f\\\"Eval Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val - 1) >=\",\n \"variable, index combination. Parameters ---------- value : str Unique name of the node.\",\n \"UNSET_SHAPE: return False for s in self.shape: if not isinstance(s, Integral): return False\",\n \"def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a context. Parameters ----------\",\n \"for var index {self} with variable shape {self.var.shape}\\\") return True else: return self.var.shape\",\n \"in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- fetches\",\n \"nodeop(operator.getitem) gt = nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv)\",\n \"of this node. If graph is `None`, this is the top-level graph. \\\"\\\"\\\"\",\n \"\\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if c.write_count > 0 else c.name fetches[fetches.index(c)] =\",\n \"return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def\",\n \"__gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"evaluate the node. callback : callable or None Callback to be evaluated when\",\n \"return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph)\",\n \"Instantiate a graph by replacing all node names with node instances. .. note::\",\n \"{key: partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs} #\",\n \"callable_ = nodeop(builtins.callable) format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_\",\n \"def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul,\",\n \"ValueError If no `Graph` instance can be obtained. \\\"\\\"\\\" graph = graph or\",\n \"value = self.target(op1, op2) else: arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx =\",\n \"context: return context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args\",\n \"associated graph. \\\"\\\"\\\" # TODO: Need a way to check if the existing\",\n \"len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not match\",\n \"an node. Returns ------- instantiated_node : Node Node instance. Raises ------ ValueError If\",\n \"self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of\",\n \"or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches] single\",\n \"nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth)\",\n \"or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else:\",\n \"nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ = nodeop(builtins.hex) next_ = nodeop(builtins.next)\",\n \"target kwargs : dict keywoard arguments passed to the target \\\"\\\"\\\" def __init__(self,\",\n \"for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float): shapes.append(int(dim))\",\n \"fetches = [fetches] single = True elif isinstance(fetches, Sequence): single = False else:\",\n \"nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ = nodeop(builtins.complex)\",\n \"`context` is not a mapping. \\\"\\\"\\\" if context is None: context = {}\",\n \"pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ---------- target : callable\",\n \"# pragma: no cover messages = [] interactive = False if isinstance(node, Node)\",\n \"this to underscore private variable self.kwargs = kwargs self.graph = graph self._shape =\",\n \"evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant given a context.\",\n \"key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key])\",\n \"parameter node names. \\\"\\\"\\\" return self._shape @property def var(self): return self @property def\",\n \"return node_hash(self) def find_node(self, name): g = self.graph while g is not None\",\n \"`Node` instances or names to evaluate. context : dict or None Context in\",\n \"def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"does not belong to this graph. \\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if\",\n \"\\\"\\\"\\\" def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs\",\n \"= [] self._args = [] self._predeecessors = [] self._succesors = [] self.args =\",\n \"list): return [partial(element) for element in node] if isinstance(node, dict): return {partial(key): partial(value)\",\n \"var_index(self, [key], graph=self) else: return self.nodes[key] def __add__(self, other): return add(self, other, graph=self.graph)\",\n \"{self.args}\\\") if self.graph: graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name,\",\n \"when an node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def\",\n \"\\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size = 5 evaluated_nodes = 0\",\n \"self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other):\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lt__(self) def __ge__(self, other): return ge(self, other,\",\n \"return \\\"/\\\".join(list(reversed(scope_names))) def __enter__(self): Node._graph_stack.append(self) return self def __exit__(self, *args): assert self ==\",\n \"name): \\\"\\\"\\\" Set the name for a variable index, making sure to replicate\",\n \"elif isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif\",\n \"enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float):\",\n \"else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape) and\",\n \"other): return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self,\",\n \"context so we can report where the node was defined self._stack = traceback.extract_stack(limit=1)\",\n \"if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif\",\n \"self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other):\",\n \"an `Node` instance, node name, or a sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str,\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self,\",\n \"# Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs)\",\n \"else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx], (func_op,\",\n \"name): self.set_name(name) @args.setter def args(self, args): new_args = [] for arg in args:\",\n \"graph=self.graph) def __truediv__(self, other): return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return\",\n \"def target(self): return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name =\",\n \"self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\"\",\n \"RuntimeError(f\\\"Cannot find {name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this\",\n \"(slice_op, var_index)) or _is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if\",\n \"output for a node. This can be a tuple of integers or parameter\",\n \"if name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name,\",\n \"tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\"\",\n \"return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional\",\n \"This function modifies the context in place. Use :code:`context=context.copy()` to avoid the context\",\n \"or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by\",\n \"node or constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if isinstance(node,\",\n \"slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def\",\n \"def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and\",\n \"greater than the variable shape:\\\\n\\\" f\\\"\\\\tArgs: {self.args}\\\\n\\\" f\\\"\\\\tVar shape: {var.shape}\\\\n\\\" f\\\"\\\\tNode shape: {self.var.shape}\\\\n\\\"\",\n \"return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph)\",\n \"\\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx, name=None, **kwargs): # pylint:\",\n \"other, self, graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self,\",\n \"bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_\",\n \"shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies\",\n \"slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args)\",\n \"== 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if\",\n \"# Stop tracing at the last interactive cell if interactive and not fname.startswith('<'):\",\n \"isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and\",\n \"name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)):\",\n \"numbers import Integral, Rational, Real import contextlib import traceback import uuid import numpy\",\n \"context being modified. Parameters ---------- fetches : list[str or Node] or str or\",\n \"and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")])\",\n \"isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node,\",\n \"the node and add the node to the graph self._name = None self.name\",\n \"return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node\",\n \"a for a in self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape of\",\n \"return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else a for\",\n \"n in node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack):\",\n \"in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var, axis=i) else: var =\",\n \"arg0_dom = self.args[0].domain arg1_dom = self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else\",\n \"node) if isinstance(node, list): return [partial(element) for element in node] if isinstance(node, dict):\",\n \"\\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii)\",\n \": str or None Name of the node or `None` to use a\",\n \"@property def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple : Positional arguments\",\n \"c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in c.name.split(\\\"/\\\")]) if\",\n \"this node. If graph is `None`, this is the top-level graph. \\\"\\\"\\\" return\",\n \"return node.evaluate(context, **kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element)\",\n \"the context in place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters\",\n \"{self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack:\",\n \"node. This can be a tuple of integers or parameter node names. graph\",\n \"indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs()\",\n \"first self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the parents\",\n \"var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None,\",\n \"node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs):\",\n \"with arguments if target is None: return functools.partial(nodeop, **kwargs) # This is called\",\n \"representing values of a variable corresponding to input index values. Parameters ---------- var\",\n \"_evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function to evaluate\",\n \"or `None` to use a random, unique identifier. shape : tuple or None\",\n \"return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray)) and len(key) ==\",\n \"= nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ =\",\n \"or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get the stack context\",\n \"node name. RuntimeError If `node` is an `Node` instance but does not belong\",\n \"---------- graph : Node or None Graph to return or `None` to use\",\n \"float): shapes.append(int(dim)) else: raise TypeError(f\\\"Shape value must be placeholder or integer value for\",\n \"**kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single = True else:\",\n \"is `None`, this is the top-level graph. \\\"\\\"\\\" return self._graph def preds(self): return\",\n \"__and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other,\",\n \"if shape and isinstance(shape[idx], (func_op, Integral)): s.append(shape[idx]) elif shape and isinstance(shape[idx], float): s.append(int(shape[idx]))\",\n \"other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other):\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return\",\n \"stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\"\",\n \"else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target\",\n \"slice1_idx = Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain\",\n \"x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2)\",\n \"return self._target @target.setter def target(self, fnc): self._target = fnc self.op_name = f\\\"{fnc.__name__}\\\" self.kwargs[\\\"target\\\"]\",\n \"target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property\",\n \"__or__(self, other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \".util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class\",\n \"set_edges(self): for e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self):\",\n \"tuple : Positional arguments which are used for executing this node. \\\"\\\"\\\" return\",\n \"self._predeecessors = [] self._succesors = [] self.args = args if \\\"name\\\" in kwargs:\",\n \"= nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul = nodeop(operator.matmul) methodcaller =\",\n \"__call__(self, *args, **kwargs): return self.run(*args, **kwargs) class EvaluationError(RuntimeError): \\\"\\\"\\\" Failed to evaluate an\",\n \"add_predecessor(self, pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ,\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def __truediv__(self, other): return truediv(self, other,\",\n \"tuple Positional arguments passed to the `_evaluate` method. name : str or None\",\n \"0 def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes =\",\n \"args(self): \\\"\\\"\\\" tuple : Positional arguments which are used for executing this node.\",\n \"partial(value) for key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate\",\n \"return var_index(self, [key], name=name, graph=self.graph) def set_shape(self, shape=None, init=False): s = [] assert\",\n \"or parameter node names. \\\"\\\"\\\" return self._shape @property def var(self): return self @property\",\n \"value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None and slice2_var is None:\",\n \"self.kwargs.items() if key not in self.added_attrs} # Evaluate the node callback = callback\",\n \"Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key)\",\n \"@property def argnames(self): return [a.name if isinstance(a, Node) else a for a in\",\n \"tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element) for\",\n \"not in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does not belong\",\n \"(tuple, list)) if all([isinstance(sv, Integral) for sv in shape]) and len(self.domain) == np.product(shape)\",\n \"Output of the node given the context. \\\"\\\"\\\" # Evaluate all explicit dependencies\",\n \"other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"old_key else (k, self.nodes[k]), self.nodes.keys())) self.nodes = Graph(n) def insert_node(self, node, idx): node_list\",\n \".graph import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash,\",\n \"values. Parameters ---------- var : Node The multi-dimensional variable used for indexing into.\",\n \"---------- target : cal The multi-dimensional variable used for indexing into. idx :\",\n \"if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args =\",\n \"*args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\ (self.name,\",\n \"= callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\": context[self] =\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph)\",\n \"\\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\"))\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return mod(self, other, graph=self.graph) if not\",\n \"def __rtruediv__(self, other): return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne,\",\n \"__radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self,\",\n \"node names. \\\"\\\"\\\" return self._shape @property def var(self): return self @property def name(self):\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if\",\n \"len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i] == 1: var =\",\n \"variable name. Returns ------- normalized_context : dict[Node, object] Normalized context whose keys are\",\n \"node name, or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in\",\n \"this is the top-level graph. op_name : str Operation name which describes the\",\n \"other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self,\",\n \"the current name of the node cannot be found in the associated graph.\",\n \"tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if shape and isinstance(shape[idx],\",\n \"lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self,\",\n \"argnames(self): return [a.name if isinstance(a, Node) else a for a in self.args] @property\",\n \"return mul(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def\",\n \"other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self)\",\n \"= nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted) ascii_ =\",\n \"return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self,\",\n \"isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self,\",\n \"yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ =\",\n \"False for s in self.shape: if not isinstance(s, Integral): return False return True\",\n \"def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self, graph=self.graph) def __pos__(self): return\",\n \"Domain(tuple([])) if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var,\",\n \"This is called when the decorator is used with arguments if target is\",\n \"other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"to\\\" f\\\" {node.graph}\\\") return node raise ValueError(f\\\"'{node}' is not an `Node` instance or\",\n \"not None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] ==\",\n \"def shape(self, shape): self.set_shape(shape, init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property\",\n \"nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals)\",\n \"\\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node): # pylint:disable=W0621\",\n \"__invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self,\",\n \"import Graph from .domain import Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\\",\n \"= list(context) # Add the keyword arguments for node in nodes: # pylint:disable=W0621\",\n \"capture any internal stack traces fname = frame.filename if 'polymath' in fname: continue\",\n \"internal stack traces fname = frame.filename if 'polymath' in fname: continue # Stop\",\n \"graph=self.graph) def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other): return\",\n \"def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod,\",\n \"rshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self,\",\n \"is used with arguments if target is None: return functools.partial(nodeop, **kwargs) # This\",\n \"If `node` is an `Node` instance but does not belong to this graph.\",\n \"other.__gt__(self) def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"name, or a \\\" \\\"sequence thereof.\\\") fetches = [self.instantiate_node(node) for node in fetches]\",\n \"context[self] # Evaluate the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg)\",\n \"np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if out_shape[i]\",\n \"= self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node)\",\n \"if isinstance(node, dict): return {partial(key): partial(value) for key, value in node.items()} if isinstance(node,\",\n \"def __repr__(self): return \\\"<node '%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key]\",\n \"# Add the keyword arguments for node in nodes: # pylint:disable=W0621 value =\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__and__(self) def __or__(self, other): return\",\n \"return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def\",\n \"init=False): s = [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv\",\n \"slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def\",\n \"= nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min) setattr_ = nodeop(builtins.setattr) all_ =\",\n \"s in self.shape: if not isinstance(s, Integral): return False return True def set_shape(self,\",\n \"= nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ =\",\n \"callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node and discard the values. Parameters\",\n \"------ ValueError If `node` is not an `Node` instance or an node name.\",\n \"node. The default hash returns an object id, whereas this function returns a\",\n \"x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None):\",\n \"other): return slice_op(operator.lshift, self, other, graph=self.graph) def __rlshift__(self, other): return slice_op(operator.lshift, other, self,\",\n \"\\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs)\",\n \"current ndoe as ewll if self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name\",\n \"is not an `Node` instance or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\"\",\n \"@args.setter def args(self, args): new_args = [] for arg in args: if isinstance(arg,\",\n \"representing multi-dimensional operations performed on a node. Parameters ---------- target : cal The\",\n \": Positional arguments which are used for executing this node. \\\"\\\"\\\" return tuple(self._args)\",\n \"other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self,\",\n \"pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv\",\n \"index combination. Parameters ---------- value : str Unique name of the node. Returns\",\n \"return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph)\",\n \"shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape =\",\n \"note:: This function modifies the context in place. Use :code:`context=context.copy()` to avoid the\",\n \"node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name if isinstance(a, Node) else\",\n \"class slice_op(Node): \\\"\\\"\\\" Node representing multi-dimensional operations performed on a node. Parameters ----------\",\n \"in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot\",\n \"*args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes should implement this function to evaluate the\",\n \"slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs)\",\n \"@functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return _wrapper @nodeop def\",\n \"Raises ------ ValueError If an node with `value` already exists in the associated\",\n \"Stack: {Node._eval_stack}\\\") if not single and not all([(idx_val - 1) >= indices[-1][idx] for\",\n \"not in g.nodes: g = g.graph if name in g.nodes: return g.nodes[name] raise\",\n \"the graph. Parameters ---------- value : str Unique name of the node. Returns\",\n \"[] interactive = False if isinstance(node, Node) or not is_iterable(node): node = [node]\",\n \"for a variable index, making sure to replicate the new name with a\",\n \"np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2,\",\n \"and update the graph. Parameters ---------- value : str Unique name of the\",\n \"var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices = (0,) single =\",\n \"set using `value`. kwargs : dict Keyword arguments passed to the `_evaluate` method.\",\n \"keys are node instances or names. kwargs : dict[str, object] Additional context information\",\n \"else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"in node: stack = [] if isinstance(n, Node): for frame in reversed(n._stack): #\",\n \"Rational, Real import contextlib import traceback import uuid import numpy as np import\",\n \"**kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list):\",\n \"`node` is an `Node` instance but does not belong to this graph. \\\"\\\"\\\"\",\n \"__matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph)\",\n \"the node object associated with the node name. Parameters ---------- node : Node\",\n \"name for a variable index, making sure to replicate the new name with\",\n \"raise TypeError(f\\\"Shape value must be placeholder or integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\"\",\n \"**kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs): \\\"\\\"\\\" Evaluate an node or constant\",\n \": Node The multi-dimensional variable used for indexing into. idx : tuple Tuple\",\n \"in nodes: # pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in\",\n \"to use the default graph. Raises ------ ValueError If no `Graph` instance can\",\n \"dict Additional context information keyed by variable name. Returns ------- values : Node\",\n \"self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node and\",\n \"oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_\",\n \"context specifies more than one value for any node. ValueError If `context` is\",\n \"arguments passed to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = []\",\n \"graph is `None`, this is the top-level graph. op_name : str Operation name\",\n \"`Node` instance or node name\\\") def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph\",\n \"Node)): fetches = [fetches] single = True elif isinstance(fetches, Sequence): single = False\",\n \"rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self,\",\n \"@op_name.setter def op_name(self, op_name): if op_name: self._op_name = op_name elif self.__class__.__name__ == \\\"Node\\\":\",\n \"the variable, index combination. Parameters ---------- value : str Unique name of the\",\n \"nodeop(builtins.type) chr_ = nodeop(builtins.chr) frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range)\",\n \"return context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or\",\n \"If graph is `None`, this is the top-level graph. op_name : str Operation\",\n \"kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: all_args = _flatten_iterable(args)\",\n \"format_ = nodeop(builtins.format) len_ = nodeop(builtins.len) property_ = nodeop(builtins.property) type_ = nodeop(builtins.type) chr_\",\n \"if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple): return var_index(self, list(key),\",\n \"out_shape = (1,) indices = (0,) single = True else: out_shape = self.domain.shape_from_indices(indices)\",\n \"this scope. Parameters ---------- dependencies : list Sequence of nodes to be evaluted\",\n \"else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self):\",\n \"= self.__class__.__name__ @name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args =\",\n \"{self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray)\",\n \"node cannot be found in the associated graph. \\\"\\\"\\\" # TODO: Need a\",\n \"f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\", **kwargs) self.target = target @property def domain(self):\",\n \"+= [\\\"domain\\\", \\\"target\\\"] @property def target(self): return self._target @target.setter def target(self, fnc): self._target\",\n \"name of the node and update the graph. Parameters ---------- value : str\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other,\",\n \"at the last interactive cell if interactive and not fname.startswith('<'): break # pragma:\",\n \"other, graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other):\",\n \"len(out_shape) and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)):\",\n \"operations performed on a node. Parameters ---------- target : cal The multi-dimensional variable\",\n \"not an `Node` instance or an node name. RuntimeError If `node` is an\",\n \"specifies more than one value for any node. ValueError If `context` is not\",\n \"args: if isinstance(arg, Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args\",\n \"= nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super) bytes_ =\",\n \"Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes =\",\n \"for the node and add the node to the graph self._name = None\",\n \"importlib from .graph import Graph from .domain import Domain from .util import _noop_callback,\",\n \"exists in the associated graph. KeyError If the current name of the node\",\n \"and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New:\",\n \"\\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate\",\n \"slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def\",\n \"[] stack_size = 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None, graph=None,\",\n \"init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape])\",\n \"which describes the node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if\",\n \"partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in\",\n \"defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of\",\n \"subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph\",\n \"None: context = {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be a\",\n \"to use a random, unique identifier. shape : tuple or None Shape of\",\n \"def __init__(self, target, *args, **kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\\",\n \"graph=self.graph) def __rand__(self, other): return slice_op(operator.and_, other, self, graph=self.graph) def __or__(self, other): return\",\n \"\\\"\\\"\\\" Base class for nodes. Parameters ---------- args : tuple Positional arguments passed\",\n \"value for node '{node}'\\\") context[node] = value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\",\",\n \"key, value in self.kwargs.items() if key not in self.added_attrs} # Evaluate the node\",\n \"return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def\",\n \"pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values of a variable corresponding to input index\",\n \"def __radd__(self, other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub,\",\n \"or None Callback to be evaluated when an node is evaluated. Returns -------\",\n \"= cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name,\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph)\",\n \"\\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches] single = True elif isinstance(fetches,\",\n \"other): return matmul(other, self, graph=self.graph) def __mul__(self, other): return mul(self, other, graph=self.graph) if\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if not\",\n \"One or more `Node` instances or names to evaluate. context : dict or\",\n \"key, name=name, graph=self.graph) elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return\",\n \"whose keys are node instances or names. kwargs : dict[str, object] Additional context\",\n \"__exit__(self, *args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"<node '%s'>\\\" % self.name\",\n \"graph : Node or None Graph to return or `None` to use the\",\n \"in fetches] context = self.instantiate_graph(context, **kwargs) for c in context: if c in\",\n \"'{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if\",\n \"self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self,\",\n \"graph=self) elif isinstance(key, tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self)\",\n \"= np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list\",\n \"Use :code:`context=context.copy()` to avoid the context being modified. Parameters ---------- context : dict[Node\",\n \"def __add__(self, other): return add(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not\",\n \"slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([]))\",\n \"self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\"\",\n \"and np.prod(var.shape) == np.prod(out_shape): if len(out_shape) > len(var.shape): for i in range(len(out_shape)): if\",\n \"= nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed) complex_ =\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self) def __rrshift__(self, other):\",\n \"of a variable corresponding to input index values. Parameters ---------- var : Node\",\n \"\\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key, (list)):\",\n \"lshift(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self,\",\n \"name, value in kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\\\"duplicate\",\n \"self.var.name + \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name]\",\n \"= Graph() self.value = value self.dependencies = [] self._args = [] self._predeecessors =\",\n \"1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if len(var.shape) != len(out_shape)\",\n \"nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne)\",\n \"else a for a in self.args] @property def shape(self): \\\"\\\"\\\" tuple : Shape\",\n \"True def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape,\",\n \"Node instance. Raises ------ ValueError If `node` is not an `Node` instance or\",\n \"return {partial(key): partial(value) for key, value in node.items()} if isinstance(node, slice): return slice(*[partial(getattr(node,\",\n \"node.name not in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does not\",\n \"isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\" +\",\n \"insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def\",\n \"return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph)\",\n \"is an `Node` instance but does not belong to this graph. \\\"\\\"\\\" if\",\n \"shape if self.shape == UNSET_SHAPE: raise TypeError(f'`shape` must be specified explicitly for nodes\",\n \"a hash of all attributes and subgraphs of a node. \\\"\\\"\\\" return node_hash(self)\",\n \"`%s` defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def\",\n \"True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional hash\",\n \"if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name + \\\"[\\\"\",\n \"last interactive cell if interactive and not fname.startswith('<'): break # pragma: no cover\",\n \"'%s'>\\\" % self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value def is_shape_finalized(self):\",\n \"ret = var_index(self.var, key, graph=self) else: ret = var_index(self.var, tuple([key]), graph=self) return ret\",\n \"isinstance(shape, Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif not\",\n \"values. .. note:: This function modifies the context in place. Use :code:`context=context.copy()` to\",\n \"ret = self.nodes[name] return ret else: name = [] if isinstance(key, Node): name.append(key.name)\",\n \"= nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow =\",\n \"{self} with variable shape {self.var.shape}\\\") return True else: return self.var.shape == DEFAULT_SHAPES[0] def\",\n \"elif self.is_shape_finalized() and len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key])\",\n \"uuid import numpy as np import importlib from .graph import Graph from .domain\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self): return neg(self,\",\n \"Call `func` with positional arguments `args` and keyword arguments `kwargs`. Parameters ---------- func\",\n \"self.evaluate_dependencies(context, callback) if self in context: return context[self] # Evaluate the parents partial\",\n \"of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph while\",\n \"\\\"\\\"\\\" if isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not in\",\n \"disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_ = nodeop(builtins.help) min_ = nodeop(builtins.min)\",\n \"import Integral, Rational, Real import contextlib import traceback import uuid import numpy as\",\n \"return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph)\",\n \"return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other, graph=self.graph)\",\n \"traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\" polymath.srdfg.graph.Graph : Parent graph of this node. If\",\n \"\\\"\\\"\\\" tuple : Shape of the output for a node. This can be\",\n \"the graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ = nodeop(builtins.dict) help_\",\n \"= nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem =\",\n \"_is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph)\",\n \"graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return\",\n \"callback=callback) for fetch in fetches] return values[0] if single else tuple(values) def __getstate__(self):\",\n \"Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property\",\n \"= [] interactive = False if isinstance(node, Node) or not is_iterable(node): node =\",\n \"= self.target(op1, op2) return value def get_index_nodes(self, slice1_var=None, slice2_var=None): if slice1_var is None\",\n \"or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target, *args, **kwargs): if \\\"domain\\\" in kwargs:\",\n \"set_name(self, name): \\\"\\\"\\\" Set the name for a variable index, making sure to\",\n \"def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if not\",\n \"e in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([]))\",\n \"le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self,\",\n \"def __repr__(self): return \\\"<var_index name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node\",\n \"Function to call when the node is executed. args : list Sequence of\",\n \"Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def\",\n \"using the default graph. Parameters ---------- graph : Node or None Graph to\",\n \"g = g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name}\",\n \"and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph,\",\n \"raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] = value if node.op_name in\",\n \"import uuid import numpy as np import importlib from .graph import Graph from\",\n \"pred): if isinstance(pred, Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node):\",\n \"is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args : tuple\",\n \"---------- node : Node or str Node instance or name of an node.\",\n \"node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value for node\",\n \"other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self, graph=self.graph) def\",\n \"isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)): if self.var.shape !=\",\n \"kwargs.items(): node = self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for node\",\n \"locals_ = nodeop(builtins.locals) repr_ = nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_\",\n \"= frame.filename if 'polymath' in fname: continue # Stop tracing at the last\",\n \"= nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem =\",\n \"{self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name and self._name in\",\n \"out_shape == (1,) and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return\",\n \"nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter)\",\n \"for element in node] if isinstance(node, dict): return {partial(key): partial(value) for key, value\",\n \"= np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value =\",\n \"context whose keys are node instances. Raises ------ ValueError If the context specifies\",\n \"def insert_node(self, node, idx): node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list)\",\n \"for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices) == 1: res\",\n \"else other.__ge__(self) def __ne__(self, other): return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self,\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other,\",\n \"Parameters ---------- target : callable function to evaluate the node args : tuple\",\n \"pylint: disable=protected-access # Do not capture any internal stack traces fname = frame.filename\",\n \"and len(indices) == 1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self,\",\n \"0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n =\",\n \"__rpow__(self, other): return pow_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this node and discard\",\n \"raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context) # Add the keyword\",\n \"list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs)\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other): return and_(self, other,\",\n \"if self.graph: graph = self.graph if self._name is not None and self._name in\",\n \"to the `_evaluate` method. \\\"\\\"\\\" _graph_stack = deque([None]) _eval_stack = [] stack_size =\",\n \"{type(var)} is not a list or numpy array, and cannot be sliced for\",\n \"nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin) eval_ = nodeop(builtins.eval)\",\n \"__init__(self, var, idx, name=None, **kwargs): # pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain\",\n \"in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key, name=name, graph=self.graph) elif\",\n \"= [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape])\",\n \"= \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to evaluate node `%s` defined at:\\\\n\\\\n%s\\\" % (n,\",\n \"while g is not None and name not in g.nodes: g = g.graph\",\n \"array, and cannot be sliced for {self.name}\\\") elif isinstance(var, list): var = np.asarray(var)\",\n \"slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other):\",\n \"range_ = nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ = nodeop(builtins.getattr) locals_\",\n \"0: if isinstance(key, (int, Node)): key = tuple([key]) if len(key) != len(self.shape): raise\",\n \"*args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args,\",\n \"isinstance(node, tuple): return tuple(partial(element) for element in node) if isinstance(node, list): return [partial(element)\",\n \"variable self.kwargs = kwargs self.graph = graph self._shape = OrderedDict() self.shape = shape\",\n \"__init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value\",\n \"graph. KeyError If the current name of the node cannot be found in\",\n \"the node. \\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context, **kwargs):\",\n \"_evaluate(self, op1, op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1, op2)\",\n \"call(self, *args, **kwargs) def __repr__(self): return \\\"<func_op '%s' target=%s args=<%d items>>\\\" % \\\\\",\n \"+ \\\"[\\\" + \\\"][\\\".join(name) + \\\"]\\\" if name in self.graph.nodes: return self.graph.nodes[name] elif\",\n \"= node.evaluate(context) return context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate\",\n \"Parameters ---------- args : tuple Positional arguments passed to the `_evaluate` method. name\",\n \"return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes and (node.graph !=\",\n \"relevant to this node self.dependencies = [] if dependencies is None else dependencies\",\n \"passed to `func`. kwargs : dict Mapping of keyword arguments passed to `func`.\",\n \"\\\"\\\"\\\" if context is None: context = {} elif not isinstance(context, Mapping): raise\",\n \"(len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs):\",\n \"nodeop(operator.gt) index = nodeop(operator.index) indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert)\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph)\",\n \"def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object\",\n \"return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph)\",\n \"Node representing values of a variable corresponding to input index values. Parameters ----------\",\n \"the explicitly given graph or using the default graph. Parameters ---------- graph :\",\n \"if not isinstance(s, Integral): return False return True def set_shape(self, shape=None, init=False): if\",\n \"nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth) xor = nodeop(operator.xor)\",\n \"countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge\",\n \"Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x,\",\n \"all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op,\",\n \"def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt,\",\n \"executed before any nodes in this scope. Parameters ---------- dependencies : list Sequence\",\n \"graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return\",\n \"next_ = nodeop(builtins.next) slice_ = nodeop(builtins.slice) any_ = nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_\",\n \"self, other, graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self,\",\n \"\\\"output\\\", \\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def run(self, fetches,\",\n \"other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def __truediv__(self, other):\",\n \"and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not match {var.shape} in {self.var.name}\",\n \"s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val):\",\n \"context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value if node.op_name in\",\n \"(self.name, self.kwargs[\\\"target\\\"], len(self.args)) def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions.\",\n \"raise ex from EvaluationError(\\\"\\\".join(messages)) @classmethod def init_from_args(cls, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None,\",\n \"be obtained. \\\"\\\"\\\" graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node):\",\n \"returns a hash of all attributes and subgraphs of a node. \\\"\\\"\\\" return\",\n \"graph=self.graph) def __ror__(self, other): return slice_op(operator.or_, other, self, graph=self.graph) def __xor__(self, other): return\",\n \"`args` and keyword arguments `kwargs`. Parameters ---------- func : callable Function to call\",\n \"self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self):\"\n]","isConversational":false}}},{"rowIdx":3475,"cells":{"ngram":{"kind":"list like","value":["models from django.utils.translation import ugettext_lazy as _ # Create your models here. class","name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to hollywood","Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to","be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False)","directed to hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def","models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to hollywood else directed","import ugettext_lazy as _ # Create your models here. class Actor(models.Model): name =","to hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self):","# if is_star he/she will be directed to hollywood else directed to commercial","# Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if","class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed","<gh_stars>0 from django.db import models from django.utils.translation import ugettext_lazy as _ # Create","django.db import models from django.utils.translation import ugettext_lazy as _ # Create your models","from django.utils.translation import ugettext_lazy as _ # Create your models here. class Actor(models.Model):","is_star he/she will be directed to hollywood else directed to commercial is_star =","django.utils.translation import ugettext_lazy as _ # Create your models here. class Actor(models.Model): name","import models from django.utils.translation import ugettext_lazy as _ # Create your models here.","your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she","if is_star he/she will be directed to hollywood else directed to commercial is_star","Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star","models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will","max_length=200) # if is_star he/she will be directed to hollywood else directed to","_ # Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) #","will be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"),","as _ # Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200)","else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self): return self.name","ugettext_lazy as _ # Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"),","he/she will be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\"is","here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be","= models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to hollywood else","from django.db import models from django.utils.translation import ugettext_lazy as _ # Create your","hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self): return"],"string":"[\n \"models from django.utils.translation import ugettext_lazy as _ # Create your models here. class\",\n \"name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed to hollywood\",\n \"Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed to\",\n \"be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False)\",\n \"directed to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def\",\n \"models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed to hollywood else directed\",\n \"import ugettext_lazy as _ # Create your models here. class Actor(models.Model): name =\",\n \"to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self):\",\n \"# if is_star he/she will be directed to hollywood else directed to commercial\",\n \"# Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if\",\n \"class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed\",\n \"<gh_stars>0 from django.db import models from django.utils.translation import ugettext_lazy as _ # Create\",\n \"django.db import models from django.utils.translation import ugettext_lazy as _ # Create your models\",\n \"from django.utils.translation import ugettext_lazy as _ # Create your models here. class Actor(models.Model):\",\n \"is_star he/she will be directed to hollywood else directed to commercial is_star =\",\n \"django.utils.translation import ugettext_lazy as _ # Create your models here. class Actor(models.Model): name\",\n \"import models from django.utils.translation import ugettext_lazy as _ # Create your models here.\",\n \"your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she\",\n \"if is_star he/she will be directed to hollywood else directed to commercial is_star\",\n \"Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star\",\n \"models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will\",\n \"max_length=200) # if is_star he/she will be directed to hollywood else directed to\",\n \"_ # Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) #\",\n \"will be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"),\",\n \"as _ # Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200)\",\n \"else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self): return self.name\",\n \"ugettext_lazy as _ # Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"),\",\n \"he/she will be directed to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is\",\n \"here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be\",\n \"= models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed to hollywood else\",\n \"from django.db import models from django.utils.translation import ugettext_lazy as _ # Create your\",\n \"hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self): return\"\n]","isConversational":false}}},{"rowIdx":3476,"cells":{"ngram":{"kind":"list like","value":["docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ == \"__main__\": main()","as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version():","open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir)","import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir)","def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull,","line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen","in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\")","for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs","with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir):","clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if","dir) def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as","the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in","C client docs. \"\"\" from __future__ import with_statement import os import shutil import","import os import shutil import socket import subprocess import time import urllib2 def","os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"):","\"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver","main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\")","os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from doxygenConfig.","print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if","Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__","time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir)","version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for","the C client docs. \"\"\" from __future__ import with_statement import os import shutil","with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\",","stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version","urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with","line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in","except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\",","socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass","in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ == \"__main__\":","\"\"\" from __future__ import with_statement import os import shutil import socket import subprocess","subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the","docs. \"\"\" from __future__ import with_statement import os import shutil import socket import","pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"],","clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir)","subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def","if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\"","clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def","as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with","def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\")","from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"):","import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\")","'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\")","\"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line","driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines():","def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f:","\"\"\"Build the C client docs. \"\"\" from __future__ import with_statement import os import","shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null:","line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in","import shutil import socket import subprocess import time import urllib2 def clean_dir(dir): try:","return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs","with open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def","open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main():","shutil import socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir)","import socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except:","gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\")","__future__ import with_statement import os import shutil import socket import subprocess import time","dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null,","stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from","clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w')","stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null:","os import shutil import socket import subprocess import time import urllib2 def clean_dir(dir):","null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get","if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating","client docs. \"\"\" from __future__ import with_statement import os import shutil import socket","version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines(): if","doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return","\"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as","gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\",","as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating","subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w')","os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null,","os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"],","os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with","with_statement import os import shutil import socket import subprocess import time import urllib2","\"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip()","'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def","f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx","from __future__ import with_statement import os import shutil import socket import subprocess import","import with_statement import os import shutil import socket import subprocess import time import","f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\")","null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull,","try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as","def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null)","def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null)","open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir)","os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\")","docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ ==","stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\",","import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir):"],"string":"[\n \"docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ == \\\"__main__\\\": main()\",\n \"as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version():\",\n \"open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir)\",\n \"import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir)\",\n \"def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull,\",\n \"line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen\",\n \"in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\")\",\n \"for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs\",\n \"with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir):\",\n \"clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if\",\n \"dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as\",\n \"the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in\",\n \"C client docs. \\\"\\\"\\\" from __future__ import with_statement import os import shutil import\",\n \"import os import shutil import socket import subprocess import time import urllib2 def\",\n \"os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"):\",\n \"\\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver\",\n \"main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\")\",\n \"os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig.\",\n \"print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if\",\n \"Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__\",\n \"time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir)\",\n \"version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for\",\n \"the C client docs. \\\"\\\"\\\" from __future__ import with_statement import os import shutil\",\n \"with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\",\",\n \"stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version\",\n \"urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with\",\n \"line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in\",\n \"except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\",\",\n \"socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass\",\n \"in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ == \\\"__main__\\\":\",\n \"\\\"\\\"\\\" from __future__ import with_statement import os import shutil import socket import subprocess\",\n \"subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the\",\n \"docs. \\\"\\\"\\\" from __future__ import with_statement import os import shutil import socket import\",\n \"pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"],\",\n \"clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir)\",\n \"subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def\",\n \"if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\"\",\n \"clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def\",\n \"as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with\",\n \"def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\")\",\n \"from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"):\",\n \"import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\")\",\n \"'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\")\",\n \"\\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line\",\n \"driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines():\",\n \"def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f:\",\n \"\\\"\\\"\\\"Build the C client docs. \\\"\\\"\\\" from __future__ import with_statement import os import\",\n \"shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null:\",\n \"line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in\",\n \"import shutil import socket import subprocess import time import urllib2 def clean_dir(dir): try:\",\n \"return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs\",\n \"with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def\",\n \"open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main():\",\n \"shutil import socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir)\",\n \"import socket import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except:\",\n \"gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\")\",\n \"__future__ import with_statement import os import shutil import socket import subprocess import time\",\n \"dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null,\",\n \"stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from\",\n \"clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w')\",\n \"stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null:\",\n \"os import shutil import socket import subprocess import time import urllib2 def clean_dir(dir):\",\n \"null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get\",\n \"if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating\",\n \"client docs. \\\"\\\"\\\" from __future__ import with_statement import os import shutil import socket\",\n \"version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if\",\n \"doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return\",\n \"\\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as\",\n \"gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\",\",\n \"as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating\",\n \"subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w')\",\n \"os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null,\",\n \"os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"],\",\n \"os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with\",\n \"with_statement import os import shutil import socket import subprocess import time import urllib2\",\n \"\\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip()\",\n \"'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def\",\n \"f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx\",\n \"from __future__ import with_statement import os import shutil import socket import subprocess import\",\n \"import with_statement import os import shutil import socket import subprocess import time import\",\n \"f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\")\",\n \"null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull,\",\n \"try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as\",\n \"def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null)\",\n \"def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null)\",\n \"open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir)\",\n \"os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\")\",\n \"docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ ==\",\n \"stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\",\",\n \"import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir):\"\n]","isConversational":false}}},{"rowIdx":3477,"cells":{"ngram":{"kind":"list like","value":["0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection =","def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location =","self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi","def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg = 0.","math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa,","= s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self","self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) ->","m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y,","GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg =","= self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self):","transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] =","scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio,","self.location = s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb):","import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale =","def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree:","self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self)","-self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m *","-10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg","rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = []","__init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location","rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return","glm.mat3: a = degree / 180. * math.pi sa = math.sin(a) ca =","* glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m =","glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg)","0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)","= 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self)","m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return","s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val,","glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return","}) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location","sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self,","glm import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"):","math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs =","= glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio","self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale,","glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m","self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self):","\"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0)","m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale,","scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4:","push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s =","math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 )","self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return","\"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg =","-> glm.mat3: a = degree / 180. * math.pi sa = math.sin(a) ca","ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio,","= self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\":","self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self def __exit__(self,","* ratio, scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self)","0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m =","exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree /","= math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1","-> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale","* math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0,","= rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack =","self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a","180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2.","m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(),","m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m","m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) )","-> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x","def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(),","glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio =","m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5","return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def","self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg","self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack","glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return m def",") m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m *","self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg,","0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection","glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale))","import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs","glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings):","= [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width /","self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4:","self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push()","= rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y","scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1),","-self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *=","* .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({","self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale =","ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m =","glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1]","glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1)","s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def","def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s","-scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate(","def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a =","def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi sa","= m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3:","-> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0,","def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m","10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg /","ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0,","ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args,","-sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs):","* glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0))","m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self):","/ 180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca,","RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10.","glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale *","1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale *","self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1.","[] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height","1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m","glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m","self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width","s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self def","= self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale,","10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) ->","= glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m","= degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a) return","1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection = GameProjection(self)","m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\":","m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m","class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg","glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m =","m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] =","m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 *","return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class","return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180","= m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x,","\"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale","m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) ->","= 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale","= math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0,","0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale =","180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa,","= glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return m","sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca,","/ self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def","__enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float)","glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m","float) -> glm.mat3: a = degree / 180. * math.pi sa = math.sin(a)","*= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def","self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"]","/ self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10,","/ 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(),","rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location =","= self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(),","self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), })",".5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\":","rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi sa =","0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale","lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale","self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10)","= s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type,","exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. *","ratio, scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) ->","glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m","= s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop()","__exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree","return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3:","def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0]","pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"]","ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def","import glm import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs:","\"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"]","self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def","transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0,","projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m =","math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0,","0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args,","return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale *","from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs","exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180.","self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1)","degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3(","s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def","* glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. /","* ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m","= 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale","a = degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a)"],"string":"[\n \"0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection =\",\n \"def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location =\",\n \"self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi\",\n \"def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg = 0.\",\n \"math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa,\",\n \"= s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self\",\n \"self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) ->\",\n \"m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y,\",\n \"GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg =\",\n \"= self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self):\",\n \"transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] =\",\n \"scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio,\",\n \"self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb):\",\n \"import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale =\",\n \"def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree:\",\n \"self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self)\",\n \"-self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m *\",\n \"-10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg\",\n \"rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = []\",\n \"__init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location\",\n \"rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return\",\n \"glm.mat3: a = degree / 180. * math.pi sa = math.sin(a) ca =\",\n \"* glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m =\",\n \"glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg)\",\n \"0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)\",\n \"= 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self)\",\n \"m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return\",\n \"s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val,\",\n \"glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return\",\n \"}) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location\",\n \"sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self,\",\n \"glm import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"):\",\n \"math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs =\",\n \"= glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio\",\n \"self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale,\",\n \"glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m\",\n \"self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self):\",\n \"\\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0)\",\n \"m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale,\",\n \"scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4:\",\n \"push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s =\",\n \"math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 )\",\n \"self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return\",\n \"\\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg =\",\n \"-> glm.mat3: a = degree / 180. * math.pi sa = math.sin(a) ca\",\n \"ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio,\",\n \"= self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\":\",\n \"self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self,\",\n \"* ratio, scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self)\",\n \"0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m =\",\n \"exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree /\",\n \"= math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1\",\n \"-> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale\",\n \"* math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0,\",\n \"= rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack =\",\n \"self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a\",\n \"180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2.\",\n \"m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(),\",\n \"m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m\",\n \"m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) )\",\n \"-> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x\",\n \"def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(),\",\n \"glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio =\",\n \"m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5\",\n \"return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def\",\n \"self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg\",\n \"self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack\",\n \"glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return m def\",\n \") m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m *\",\n \"self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg,\",\n \"0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection\",\n \"glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale))\",\n \"import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs\",\n \"glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings):\",\n \"= [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width /\",\n \"self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4:\",\n \"self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push()\",\n \"= rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y\",\n \"scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1),\",\n \"-self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *=\",\n \"* .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({\",\n \"self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale =\",\n \"ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m =\",\n \"glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1]\",\n \"glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1)\",\n \"s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def\",\n \"def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s\",\n \"-scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate(\",\n \"def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a =\",\n \"def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi sa\",\n \"= m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3:\",\n \"-> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0,\",\n \"def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m\",\n \"10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg /\",\n \"ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0,\",\n \"ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args,\",\n \"-sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs):\",\n \"* glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0))\",\n \"m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self):\",\n \"/ 180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca,\",\n \"RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10.\",\n \"glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale *\",\n \"1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale *\",\n \"self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1.\",\n \"[] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height\",\n \"1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m\",\n \"glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m\",\n \"self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width\",\n \"s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def\",\n \"= self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale,\",\n \"10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) ->\",\n \"= glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m\",\n \"= degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a) return\",\n \"1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection = GameProjection(self)\",\n \"m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\":\",\n \"m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m\",\n \"class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg\",\n \"glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m =\",\n \"m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1] =\",\n \"m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 *\",\n \"return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class\",\n \"return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180\",\n \"= m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x,\",\n \"\\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale\",\n \"m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) ->\",\n \"= 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale\",\n \"= math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0,\",\n \"0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale =\",\n \"180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa,\",\n \"= glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10) return m\",\n \"sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca,\",\n \"/ self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def\",\n \"__enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float)\",\n \"glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m\",\n \"float) -> glm.mat3: a = degree / 180. * math.pi sa = math.sin(a)\",\n \"*= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return m def\",\n \"self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"]\",\n \"/ self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10,\",\n \"/ 180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(),\",\n \"rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location =\",\n \"= self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(),\",\n \"self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), })\",\n \".5 m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\":\",\n \"rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. * math.pi sa =\",\n \"0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale\",\n \"lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale\",\n \"self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10)\",\n \"= s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type,\",\n \"exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. *\",\n \"ratio, scale * ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) ->\",\n \"glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m\",\n \"= s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop()\",\n \"__exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree\",\n \"return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3:\",\n \"def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0]\",\n \"pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"]\",\n \"ca, sa, 0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def\",\n \"import glm import math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs:\",\n \"\\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"]\",\n \"self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def\",\n \"transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0,\",\n \"projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m =\",\n \"math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0,\",\n \"0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args,\",\n \"return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale *\",\n \"from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs\",\n \"exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180.\",\n \"self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1)\",\n \"degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3(\",\n \"s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def\",\n \"* glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. /\",\n \"* ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m\",\n \"= 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale\",\n \"a = degree / 180. * math.pi sa = math.sin(a) ca = math.cos(a)\"\n]","isConversational":false}}},{"rowIdx":3478,"cells":{"ngram":{"kind":"list like","value":["= get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values]))","\"between test times for each test are smaller than the threshold and the","urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS","cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE,","test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def","import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS =","0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] =","float ) -> bool: # check that their keys are the same if","def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return","float]) -> Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items()","set of test cases have not \" \"changed, we will export the stats","import json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import","time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents","in reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format","= \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def","\", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser =","have not \" \"changed, we will export the stats already in stats/slow-tests.json. Else,","a running test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool =","that their keys are the same if calculated_times.keys() != other_times.keys(): return False for","Else, we will export the calculated \" \"results. The default threshold is 10%.\",","slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\",","job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items():","report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled is","for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in","any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for","not \" \"changed, we will export the stats already in stats/slow-tests.json. Else, we","url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar(","( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any","= other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if","continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case","test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time)","} def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\")","common_utils.py (where this data will be used), and also matches what the output","keys are the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time","-> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow test cases in","entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list)","handled is version 2 only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"])","float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs","Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: # check that","import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import","unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a","with stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between test times for","float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def","in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url",">= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents =","test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False return True","abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return","\"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an","file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar(","path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json","name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"])","report in reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3","also matches what the output # of a running test would look like.","will export the stats already in stats/slow-tests.json. Else, we will export the calculated","float], other_times: Dict[str, float], threshold: float ) -> bool: # check that their","check that their keys are the same if calculated_times.keys() != other_times.keys(): return False","each test are smaller than the threshold and the set of test cases","times from previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\",","v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue","other_times: Dict[str, float], threshold: float ) -> bool: # check that their keys","time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]:",".pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in","cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC","[\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") #","if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times =","get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will","def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow test","results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between test times","if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time =","Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict,","statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, )","slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() ->","= [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\")","export the calculated \" \"results. The default threshold is 10%.\", ) return parser.parse_args()","threshold: return False return True def export_slow_tests(options: Any) -> None: filename = options.filename","= test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\")","default threshold is 10%.\", ) return parser.parse_args() def main() -> None: options =","output # of a running test would look like. name = f\"{casename} (__main__.{suitename})\"","for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]:","test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file","like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded:","a __main__ as that matches the format of test.__class__ in # common_utils.py (where","filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as","for report in reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise","test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump(","is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in","argparse.ArgumentParser( description=\"Export a JSON of slow test cases in PyTorch unit test suite\"","slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True )","get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str,","const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \"","the calculated \" \"results. The default threshold is 10%.\", ) return parser.parse_args() def","type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences","calculated \" \"results. The default threshold is 10%.\", ) return parser.parse_args() def main()","\"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the","test are smaller than the threshold and the set of test cases have","# check that their keys are the same if calculated_times.keys() != other_times.keys(): return","test times for each test are smaller than the threshold and the set","the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items():","): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True","Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from urllib.request import","argparse import json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser","\": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export","too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \",","threshold is 10%.\", ) return parser.parse_args() def main() -> None: options = parse_args()","separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(","statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str,","calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time)","# an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]]","-> bool: # check that their keys are the same if calculated_times.keys() !=","are smaller than the threshold and the set of test cases have not","test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) /","= test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case,","{filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs:","default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test times from previous","-> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\",","matches the format of test.__class__ in # common_utils.py (where this data will be","return { test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC","from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict,","\"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a","times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return {","cases have not \" \"changed, we will export the stats already in stats/slow-tests.json.","-> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be","below attaches a __main__ as that matches the format of test.__class__ in #","= cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for","filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case, time","than the threshold and the set of test cases have not \" \"changed,","1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version","type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2 only\") v2report =","smaller than the threshold and the set of test cases have not \"","what the output # of a running test would look like. name =","if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\"","# of a running test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded:","2 only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in","this data will be used), and also matches what the output # of","to dump slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\",","= urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times:","parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow test cases",") return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__","test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \":","= defaultdict(list) for report in reports: if report.get(\"format_version\", 1) != 2: # type:","typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE =","\"changed, we will export the stats already in stats/slow-tests.json. Else, we will export","be used), and also matches what the output # of a running test","S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares","time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests()","{ test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC }","tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict,","os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report,","for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in","if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() }","calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case]","os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float]","Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0","collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing","JSON of slow test cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\",","= get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times,","\"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str,","import argparse import json import os import statistics from collections import defaultdict from","\" \"between test times for each test are smaller than the threshold and","help=\"Specify a file path to dump slow test times from previous S3 stats.","import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List,","used), and also matches what the output # of a running test would","float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples","defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast,","float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float )","the threshold and the set of test cases have not \" \"changed, we","for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() ->","we will export the stats already in stats/slow-tests.json. Else, we will export the","= argparse.ArgumentParser( description=\"Export a JSON of slow test cases in PyTorch unit test","in # common_utils.py (where this data will be used), and also matches what","max(other_test_time, test_time) ) if relative_difference > threshold: return False return True def export_slow_tests(options:","existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times())","format of test.__class__ in # common_utils.py (where this data will be used), and","of a running test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool","Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like","(where this data will be used), and also matches what the output #","(\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports:","# common_utils.py (where this data will be used), and also matches what the","pytorch/test-infra. If the relative differences \" \"between test times for each test are","look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if","If the relative differences \" \"between test times for each test are smaller","/ max(other_test_time, test_time) ) if relative_difference > threshold: return False return True def","only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS):","in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE,","urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str,","(__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if","if relative_difference > threshold: return False return True def export_slow_tests(options: Any) -> None:","report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in","test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def","[values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\", 1)","def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case,","that matches the format of test.__class__ in # common_utils.py (where this data will","get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from","description=\"Export a JSON of slow test cases in PyTorch unit test suite\" )","= 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str,","slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument(","import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch,","import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\"","if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\"","reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently","running test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"]","their keys are the same if calculated_times.keys() != other_times.keys(): return False for test_case,","a file path to dump slow test times from previous S3 stats. Default","and the set of test cases have not \" \"changed, we will export","test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times","the stats already in stats/slow-tests.json. Else, we will export the calculated \" \"results.","version 2 only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name","as that matches the format of test.__class__ in # common_utils.py (where this data","False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time","test.__class__ in # common_utils.py (where this data will be used), and also matches","in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename,","#!/usr/bin/env python3 import argparse import json import os import statistics from collections import","def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool:","test cases have not \" \"changed, we will export the stats already in","will export the calculated \" \"results. The default threshold is 10%.\", ) return","suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below attaches","get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file,","f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return {",") parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to","\"results. The default threshold is 10%.\", ) return parser.parse_args() def main() -> None:","help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between","- test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False return","indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser","in test_suite[\"cases\"].items(): # The below attaches a __main__ as that matches the format","in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time,","path to dump slow test times from previous S3 stats. Default file path:","IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\",","(__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case:","relative differences \" \"between test times for each test are smaller than the","if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values():","succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times)","-> Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items() if","is 10%.\", ) return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options)","if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled","test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below attaches a","relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference >","test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) ->","reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\"","test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return","= f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return","} def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for","cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file","import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report,","10%.\", ) return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if","from typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE","-> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float],",") from typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen","test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time","for each test are smaller than the threshold and the set of test","from previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float,","urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\",","test_time) ) if relative_difference > threshold: return False return True def export_slow_tests(options: Any)","return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs(","stats/slow-tests.json. Else, we will export the calculated \" \"results. The default threshold is","test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str,","\".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times()","for casename, test_case in test_suite[\"cases\"].items(): # The below attaches a __main__ as that","float]: return { test_case: time for test_case, time in test_cases_dict.items() if time >=","if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str,","would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None","for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time -","cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float","options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"),","in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): #","casename, test_case in test_suite[\"cases\"].items(): # The below attaches a __main__ as that matches","slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times,","float], threshold: float ) -> bool: # check that their keys are the","Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from urllib.request","sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON","of test cases have not \" \"changed, we will export the stats already","None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items()","are the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in","v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The","filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times","test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict:","parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__ == \"__main__\":","2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2 only\")","bool: # check that their keys are the same if calculated_times.keys() != other_times.keys():","IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename,","contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float],","too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: #","a JSON of slow test cases in PyTorch unit test suite\" ) parser.add_argument(","Dict[str, float], threshold: float ) -> bool: # check that their keys are","SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"]","> threshold: return False return True def export_slow_tests(options: Any) -> None: filename =","argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow test cases in PyTorch","test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\", 1) !=","open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict =","the format of test.__class__ in # common_utils.py (where this data will be used),","other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference","with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict","stats already in stats/slow-tests.json. Else, we will export the calculated \" \"results. The","List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD","currently handled is version 2 only\") v2report = cast(Version2Report, report) if any(job_name in","RuntimeError(\"S3 format currently handled is version 2 only\") v2report = cast(Version2Report, report) if","file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow","= options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file:","get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times:","Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD =","return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str,","test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below attaches a __main__ as","SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url,","nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test times","\"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test",") -> bool: # check that their keys are the same if calculated_times.keys()","stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated","from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from","None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\")","test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items():","# type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2 only\") v2report","60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]:","DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC =","List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\", 1) != 2: #","threshold: float ) -> bool: # check that their keys are the same","return True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting","suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path","format currently handled is version 2 only\") v2report = cast(Version2Report, report) if any(job_name","# The below attaches a __main__ as that matches the format of test.__class__","python3 import argparse import json import os import statistics from collections import defaultdict","def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry","dump slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\", )","succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def","Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items() if time","differences \" \"between test times for each test are smaller than the threshold","we will export the calculated \" \"results. The default threshold is 10%.\", )","export the stats already in stats/slow-tests.json. Else, we will export the calculated \"","parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If","\"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow","SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() ->","print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] =","is version 2 only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for","in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for","return False return True def export_slow_tests(options: Any) -> None: filename = options.filename if","test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url =","The below attaches a __main__ as that matches the format of test.__class__ in","attaches a __main__ as that matches the format of test.__class__ in # common_utils.py","test_case in test_suite[\"cases\"].items(): # The below attaches a __main__ as that matches the","as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if","relative_difference > threshold: return False return True def export_slow_tests(options: Any) -> None: filename","in stats/slow-tests.json. Else, we will export the calculated \" \"results. The default threshold","test_suite[\"cases\"].items(): # The below attaches a __main__ as that matches the format of","options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times:","test cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str,","threshold and the set of test cases have not \" \"changed, we will","options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict","same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time","def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file:","-> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename,","the set of test cases have not \" \"changed, we will export the","def main() -> None: options = parse_args() export_slow_tests(options) if __name__ == \"__main__\": main()","__main__ as that matches the format of test.__class__ in # common_utils.py (where this","bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for","ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2 only\") v2report = cast(Version2Report,","= filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ):","json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import (",") file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of","test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is","generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between test","an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] =","slow test cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\",","parser = argparse.ArgumentParser( description=\"Export a JSON of slow test cases in PyTorch unit","raise RuntimeError(\"S3 format currently handled is version 2 only\") v2report = cast(Version2Report, report)","for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below","Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case, time in","List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" ->","defaultdict(list) for report in reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc]","timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float],","DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\", 1) != 2:","json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args()","parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump","of test.__class__ in # common_utils.py (where this data will be used), and also","will be used), and also matches what the output # of a running","False return True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename):","True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent","Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with","be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report","RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports:","matches what the output # of a running test would look like. name","like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in","in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case:","already in stats/slow-tests.json. Else, we will export the calculated \" \"results. The default","const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test times from previous S3","!= other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference","(other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False","other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference =","type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test times from","\"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests()","nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative","return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__ ==","in pytorch/test-infra. If the relative differences \" \"between test times for each test","str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite","{ test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float])","Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents))","calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: # check","<gh_stars>1-10 #!/usr/bin/env python3 import argparse import json import os import statistics from collections","from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1","= \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times:","of slow test cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\",","json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) ->","test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\",","other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) )","and also matches what the output # of a running test would look","file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with","!= 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2","PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify","file path to dump slow test times from previous S3 stats. Default file","data will be used), and also matches what the output # of a","Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results","\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str,","= 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report]","file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace:","the relative differences \" \"between test times for each test are smaller than","export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\")","\" \"results. The default threshold is 10%.\", ) return parser.parse_args() def main() ->","file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if",") parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra.","times for each test are smaller than the threshold and the set of","previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD,","return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold:",") if relative_difference > threshold: return False return True def export_slow_tests(options: Any) ->","The default threshold is 10%.\", ) return parser.parse_args() def main() -> None: options","will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for","\" \"changed, we will export the stats already in stats/slow-tests.json. Else, we will","= abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold:","stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between test times for each","in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below attaches a __main__","the output # of a running test would look like. name = f\"{casename}"],"string":"[\n \"= get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values]))\",\n \"\\\"between test times for each test are smaller than the threshold and the\",\n \"urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS\",\n \"cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE,\",\n \"test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def\",\n \"import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS =\",\n \"0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] =\",\n \"float ) -> bool: # check that their keys are the same if\",\n \"def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return\",\n \"float]) -> Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items()\",\n \"set of test cases have not \\\" \\\"changed, we will export the stats\",\n \"import json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import\",\n \"time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents\",\n \"in reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format\",\n \"= \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def\",\n \"\\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser =\",\n \"have not \\\" \\\"changed, we will export the stats already in stats/slow-tests.json. Else,\",\n \"a running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool =\",\n \"that their keys are the same if calculated_times.keys() != other_times.keys(): return False for\",\n \"Else, we will export the calculated \\\" \\\"results. The default threshold is 10%.\\\",\",\n \"slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\",\",\n \"job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items():\",\n \"report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is\",\n \"for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in\",\n \"any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for\",\n \"not \\\" \\\"changed, we will export the stats already in stats/slow-tests.json. Else, we\",\n \"url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar(\",\n \"( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any\",\n \"= other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if\",\n \"continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case\",\n \"test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time)\",\n \"} def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\")\",\n \"common_utils.py (where this data will be used), and also matches what the output\",\n \"keys are the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time\",\n \"-> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in\",\n \"entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list)\",\n \"handled is version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"])\",\n \"float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs\",\n \"Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: # check that\",\n \"import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import\",\n \"unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a\",\n \"with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between test times for\",\n \"float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def\",\n \"in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url\",\n \">= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents =\",\n \"test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False return True\",\n \"abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return\",\n \"\\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an\",\n \"file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar(\",\n \"path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json\",\n \"name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"])\",\n \"report in reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3\",\n \"also matches what the output # of a running test would look like.\",\n \"will export the stats already in stats/slow-tests.json. Else, we will export the calculated\",\n \"float], other_times: Dict[str, float], threshold: float ) -> bool: # check that their\",\n \"check that their keys are the same if calculated_times.keys() != other_times.keys(): return False\",\n \"each test are smaller than the threshold and the set of test cases\",\n \"times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\",\",\n \"v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue\",\n \"other_times: Dict[str, float], threshold: float ) -> bool: # check that their keys\",\n \"time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]:\",\n \".pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in\",\n \"cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC\",\n \"[\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") #\",\n \"if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times =\",\n \"get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will\",\n \"def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test\",\n \"results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between test times\",\n \"if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time =\",\n \"Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict,\",\n \"statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, )\",\n \"slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() ->\",\n \"= [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\")\",\n \"export the calculated \\\" \\\"results. The default threshold is 10%.\\\", ) return parser.parse_args()\",\n \"threshold: return False return True def export_slow_tests(options: Any) -> None: filename = options.filename\",\n \"= test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\")\",\n \"default threshold is 10%.\\\", ) return parser.parse_args() def main() -> None: options =\",\n \"output # of a running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\"\",\n \"for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]:\",\n \"test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file\",\n \"like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded:\",\n \"a __main__ as that matches the format of test.__class__ in # common_utils.py (where\",\n \"filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as\",\n \"for report in reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise\",\n \"test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump(\",\n \"is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in\",\n \"argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in PyTorch unit test suite\\\"\",\n \"slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True )\",\n \"get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str,\",\n \"const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\"\",\n \"the calculated \\\" \\\"results. The default threshold is 10%.\\\", ) return parser.parse_args() def\",\n \"type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences\",\n \"calculated \\\" \\\"results. The default threshold is 10%.\\\", ) return parser.parse_args() def main()\",\n \"\\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the\",\n \"test are smaller than the threshold and the set of test cases have\",\n \"# check that their keys are the same if calculated_times.keys() != other_times.keys(): return\",\n \"test times for each test are smaller than the threshold and the set\",\n \"the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items():\",\n \"): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True\",\n \"Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from urllib.request import\",\n \"argparse import json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser\",\n \"\\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export\",\n \"too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\",\",\n \"threshold is 10%.\\\", ) return parser.parse_args() def main() -> None: options = parse_args()\",\n \"separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(\",\n \"statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str,\",\n \"calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time)\",\n \"# an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]]\",\n \"-> bool: # check that their keys are the same if calculated_times.keys() !=\",\n \"are smaller than the threshold and the set of test cases have not\",\n \"test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) /\",\n \"= test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case,\",\n \"{filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs:\",\n \"default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test times from previous\",\n \"-> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\",\",\n \"matches the format of test.__class__ in # common_utils.py (where this data will be\",\n \"return { test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC\",\n \"from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict,\",\n \"\\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a\",\n \"times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return {\",\n \"cases have not \\\" \\\"changed, we will export the stats already in stats/slow-tests.json.\",\n \"-> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be\",\n \"below attaches a __main__ as that matches the format of test.__class__ in #\",\n \"= cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for\",\n \"filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case, time\",\n \"than the threshold and the set of test cases have not \\\" \\\"changed,\",\n \"1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version\",\n \"type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report =\",\n \"smaller than the threshold and the set of test cases have not \\\"\",\n \"what the output # of a running test would look like. name =\",\n \"if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\"\",\n \"# of a running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded:\",\n \"2 only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in\",\n \"this data will be used), and also matches what the output # of\",\n \"to dump slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\",\",\n \"= urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times:\",\n \"parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases\",\n \") return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__\",\n \"test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\":\",\n \"= defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) != 2: # type:\",\n \"typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE =\",\n \"\\\"changed, we will export the stats already in stats/slow-tests.json. Else, we will export\",\n \"be used), and also matches what the output # of a running test\",\n \"S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares\",\n \"time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests()\",\n \"{ test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC }\",\n \"tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict,\",\n \"os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report,\",\n \"for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in\",\n \"if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() }\",\n \"calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case]\",\n \"os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float]\",\n \"Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0\",\n \"collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing\",\n \"JSON of slow test cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\",\",\n \"= get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times,\",\n \"\\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str,\",\n \"import argparse import json import os import statistics from collections import defaultdict from\",\n \"\\\" \\\"between test times for each test are smaller than the threshold and\",\n \"help=\\\"Specify a file path to dump slow test times from previous S3 stats.\",\n \"import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List,\",\n \"used), and also matches what the output # of a running test would\",\n \"float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples\",\n \"defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast,\",\n \"float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float )\",\n \"the threshold and the set of test cases have not \\\" \\\"changed, we\",\n \"for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() ->\",\n \"we will export the stats already in stats/slow-tests.json. Else, we will export the\",\n \"= argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in PyTorch unit test\",\n \"in # common_utils.py (where this data will be used), and also matches what\",\n \"max(other_test_time, test_time) ) if relative_difference > threshold: return False return True def export_slow_tests(options:\",\n \"existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times())\",\n \"format of test.__class__ in # common_utils.py (where this data will be used), and\",\n \"of a running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool\",\n \"Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like\",\n \"(where this data will be used), and also matches what the output #\",\n \"(\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports:\",\n \"# common_utils.py (where this data will be used), and also matches what the\",\n \"pytorch/test-infra. If the relative differences \\\" \\\"between test times for each test are\",\n \"look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if\",\n \"If the relative differences \\\" \\\"between test times for each test are smaller\",\n \"/ max(other_test_time, test_time) ) if relative_difference > threshold: return False return True def\",\n \"only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS):\",\n \"in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE,\",\n \"urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str,\",\n \"(__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if\",\n \"if relative_difference > threshold: return False return True def export_slow_tests(options: Any) -> None:\",\n \"report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in\",\n \"test_case: time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def\",\n \"[values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1)\",\n \"def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case,\",\n \"that matches the format of test.__class__ in # common_utils.py (where this data will\",\n \"get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from\",\n \"description=\\\"Export a JSON of slow test cases in PyTorch unit test suite\\\" )\",\n \"= 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str,\",\n \"slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument(\",\n \"import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch,\",\n \"import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\"\",\n \"if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\"\",\n \"reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently\",\n \"running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"]\",\n \"their keys are the same if calculated_times.keys() != other_times.keys(): return False for test_case,\",\n \"a file path to dump slow test times from previous S3 stats. Default\",\n \"and the set of test cases have not \\\" \\\"changed, we will export\",\n \"test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times\",\n \"the stats already in stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results.\",\n \"version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name\",\n \"as that matches the format of test.__class__ in # common_utils.py (where this data\",\n \"False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time\",\n \"test.__class__ in # common_utils.py (where this data will be used), and also matches\",\n \"in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename,\",\n \"#!/usr/bin/env python3 import argparse import json import os import statistics from collections import\",\n \"def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool:\",\n \"test cases have not \\\" \\\"changed, we will export the stats already in\",\n \"will export the calculated \\\" \\\"results. The default threshold is 10%.\\\", ) return\",\n \"suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches\",\n \"get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file,\",\n \"f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return {\",\n \") parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to\",\n \"\\\"results. The default threshold is 10%.\\\", ) return parser.parse_args() def main() -> None:\",\n \"help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between\",\n \"- test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False return\",\n \"indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser\",\n \"in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that matches the format\",\n \"in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time,\",\n \"path to dump slow test times from previous S3 stats. Default file path:\",\n \"IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\",\",\n \"(__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case:\",\n \"relative differences \\\" \\\"between test times for each test are smaller than the\",\n \"if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values():\",\n \"succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times)\",\n \"-> Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items() if\",\n \"is 10%.\\\", ) return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options)\",\n \"if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled\",\n \"test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a\",\n \"relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference >\",\n \"test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) ->\",\n \"reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\"\",\n \"test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return\",\n \"= f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return\",\n \"} def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time for\",\n \"cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file\",\n \"import statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report,\",\n \"10%.\\\", ) return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if\",\n \"from typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE\",\n \"-> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float],\",\n \") from typing import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen\",\n \"test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time\",\n \"for each test are smaller than the threshold and the set of test\",\n \"from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float,\",\n \"urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\",\",\n \"test_time) ) if relative_difference > threshold: return False return True def export_slow_tests(options: Any)\",\n \"return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs(\",\n \"stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results. The default threshold is\",\n \"test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str,\",\n \"\\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times()\",\n \"for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that\",\n \"float]: return { test_case: time for test_case, time in test_cases_dict.items() if time >=\",\n \"if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str,\",\n \"would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None\",\n \"for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time -\",\n \"cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float\",\n \"options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"),\",\n \"in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): #\",\n \"casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that matches\",\n \"slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times,\",\n \"float], threshold: float ) -> bool: # check that their keys are the\",\n \"Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any from urllib.request\",\n \"sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON\",\n \"of test cases have not \\\" \\\"changed, we will export the stats already\",\n \"None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items()\",\n \"are the same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in\",\n \"v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The\",\n \"filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times\",\n \"test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict:\",\n \"parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__ == \\\"__main__\\\":\",\n \"2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\")\",\n \"bool: # check that their keys are the same if calculated_times.keys() != other_times.keys():\",\n \"IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename,\",\n \"contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float],\",\n \"too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: #\",\n \"a JSON of slow test cases in PyTorch unit test suite\\\" ) parser.add_argument(\",\n \"Dict[str, float], threshold: float ) -> bool: # check that their keys are\",\n \"SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"]\",\n \"> threshold: return False return True def export_slow_tests(options: Any) -> None: filename =\",\n \"argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in PyTorch\",\n \"test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) !=\",\n \"open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict =\",\n \"the format of test.__class__ in # common_utils.py (where this data will be used),\",\n \"other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference\",\n \"with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict\",\n \"stats already in stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results. The\",\n \"List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD\",\n \"currently handled is version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name in\",\n \"RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report = cast(Version2Report, report) if\",\n \"file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow\",\n \"= options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file:\",\n \"get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times:\",\n \"Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD =\",\n \"return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str,\",\n \"test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as\",\n \"SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url,\",\n \"nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test times\",\n \"\\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test\",\n \") -> bool: # check that their keys are the same if calculated_times.keys()\",\n \"stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated\",\n \"from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from\",\n \"None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\")\",\n \"test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items():\",\n \"# type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report\",\n \"60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]:\",\n \"DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC =\",\n \"List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) != 2: #\",\n \"threshold: float ) -> bool: # check that their keys are the same\",\n \"return True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting\",\n \"suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path\",\n \"format currently handled is version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name\",\n \"# The below attaches a __main__ as that matches the format of test.__class__\",\n \"python3 import argparse import json import os import statistics from collections import defaultdict\",\n \"def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry\",\n \"dump slow test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", )\",\n \"succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def\",\n \"Dict[str, float]: return { test_case: time for test_case, time in test_cases_dict.items() if time\",\n \"differences \\\" \\\"between test times for each test are smaller than the threshold\",\n \"we will export the calculated \\\" \\\"results. The default threshold is 10%.\\\", )\",\n \"export the stats already in stats/slow-tests.json. Else, we will export the calculated \\\"\",\n \"parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If\",\n \"\\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow\",\n \"SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() ->\",\n \"print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] =\",\n \"is version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for\",\n \"in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for\",\n \"return False return True def export_slow_tests(options: Any) -> None: filename = options.filename if\",\n \"test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url =\",\n \"The below attaches a __main__ as that matches the format of test.__class__ in\",\n \"attaches a __main__ as that matches the format of test.__class__ in # common_utils.py\",\n \"test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that matches the\",\n \"as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if\",\n \"relative_difference > threshold: return False return True def export_slow_tests(options: Any) -> None: filename\",\n \"in stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results. The default threshold\",\n \"test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that matches the format of\",\n \"options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times:\",\n \"test cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str,\",\n \"threshold and the set of test cases have not \\\" \\\"changed, we will\",\n \"options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict\",\n \"same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time\",\n \"def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file:\",\n \"-> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename,\",\n \"the set of test cases have not \\\" \\\"changed, we will export the\",\n \"def main() -> None: options = parse_args() export_slow_tests(options) if __name__ == \\\"__main__\\\": main()\",\n \"__main__ as that matches the format of test.__class__ in # common_utils.py (where this\",\n \"bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for\",\n \"ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report = cast(Version2Report,\",\n \"= filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ):\",\n \"json import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import (\",\n \") file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of\",\n \"test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is\",\n \"generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between test\",\n \"an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] =\",\n \"slow test cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\",\",\n \"parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in PyTorch unit\",\n \"raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report = cast(Version2Report, report)\",\n \"for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below\",\n \"Dict[str, float]) -> Dict[str, float]: return { test_case: time for test_case, time in\",\n \"List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" ->\",\n \"defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc]\",\n \"timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float],\",\n \"DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) != 2:\",\n \"json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args()\",\n \"parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump\",\n \"of test.__class__ in # common_utils.py (where this data will be used), and also\",\n \"will be used), and also matches what the output # of a running\",\n \"False return True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename):\",\n \"True def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent\",\n \"Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with\",\n \"be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report\",\n \"RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports:\",\n \"matches what the output # of a running test would look like. name\",\n \"like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in\",\n \"in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case:\",\n \"already in stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results. The default\",\n \"const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test times from previous S3\",\n \"!= other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference\",\n \"(other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return False\",\n \"other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference =\",\n \"type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test times from\",\n \"\\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests()\",\n \"nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative\",\n \"return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if __name__ ==\",\n \"in pytorch/test-infra. If the relative differences \\\" \\\"between test times for each test\",\n \"str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite\",\n \"{ test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float])\",\n \"Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents))\",\n \"calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool: # check\",\n \"<gh_stars>1-10 #!/usr/bin/env python3 import argparse import json import os import statistics from collections\",\n \"from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1\",\n \"= \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times:\",\n \"of slow test cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\",\",\n \"json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) ->\",\n \"test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\",\",\n \"other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) )\",\n \"and also matches what the output # of a running test would look\",\n \"file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with\",\n \"!= 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2\",\n \"PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify\",\n \"file path to dump slow test times from previous S3 stats. Default file\",\n \"data will be used), and also matches what the output # of a\",\n \"Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results\",\n \"\\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str,\",\n \"= 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report]\",\n \"file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace:\",\n \"the relative differences \\\" \\\"between test times for each test are smaller than\",\n \"export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\")\",\n \"\\\" \\\"results. The default threshold is 10%.\\\", ) return parser.parse_args() def main() ->\",\n \"file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if\",\n \") parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra.\",\n \"times for each test are smaller than the threshold and the set of\",\n \"previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD,\",\n \"return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold:\",\n \") if relative_difference > threshold: return False return True def export_slow_tests(options: Any) ->\",\n \"The default threshold is 10%.\\\", ) return parser.parse_args() def main() -> None: options\",\n \"will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for\",\n \"\\\" \\\"changed, we will export the stats already in stats/slow-tests.json. Else, we will\",\n \"= abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference > threshold:\",\n \"stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between test times for each\",\n \"in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__\",\n \"the output # of a running test would look like. name = f\\\"{casename}\"\n]","isConversational":false}}},{"rowIdx":3479,"cells":{"ngram":{"kind":"list like","value":["\"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if","j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, )","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns =","x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error,","importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step","axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) *","2.0, n - 1) return m - h, m + h def mse_loss(x,","where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights","axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return","in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action","Take in samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories","estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit","As the raw trajectories are of various lengths, the shorter ones are filled","reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length(","samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding","logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES","num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards,","( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(),","* interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights,","data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping","/= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values,","num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights)","= {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res","np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step","= se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m","discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, :","+ j_step_bias.T * j_step_bias # minimize mse error constraint = {\"type\": \"eq\", \"fun\":","Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def","importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean","int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs =","for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1","): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i","num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts),","np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0,","whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8","/ 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in","1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:,","sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1)","/ num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace(","infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) )","+ 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate",") estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities","target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights =","for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate","importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point","+ 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, :","x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for","1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] =","mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x =",") bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values)","@staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros","j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, :","All rights reserved. import itertools import logging import numpy as np import scipy","numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate","25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma):","( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,","raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator,","to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1","0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends =","rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities, etc.)","lists of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are","axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities,","compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns,","for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x,","trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories","action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length(","int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets","args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return","else: # break trajectories into several subsets to estimate confidence bounds infinite_step_returns =","import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import","mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns,","num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories","as np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from","mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0","minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0}","actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2:","j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] *","control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step +","1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )","affiliates. All rights reserved. import itertools import logging import numpy as np import","def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights,","in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories =","= np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns >","m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error), x.T)","1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions])","def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories","episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories =","WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower","return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x),","m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) /","+ 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories =",") x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards,","j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length -","with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for","weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets","random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in","and output lists of equal-length trajectories (episodes) accoriding to terminals. As the raw","episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end +","importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack(","+ 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step","np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1, )","estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2","np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step","= np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 )","Inc. and its affiliates. All rights reserved. import itertools import logging import numpy","= actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1)","* j_step_bias # minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda x:","= to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, )","= [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory])","whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier","- 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0,","#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.","j_step + 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value =","abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust,","= np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher","x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities,","np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights,","np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier,","def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0])","constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x,","np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in","(c) Facebook, Inc. and its affiliates. All rights reserved. import itertools import logging","= 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals)","import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL =","mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards,",") importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(","bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT","1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = []","): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1))","1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1]","estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1],","trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1","num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i +","= covariance + j_step_bias.T * j_step_bias # minimize mse error constraint = {\"type\":","shorter ones are filled with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0])","* 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts","num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1:","return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights",") = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0]","trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights","decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns <","- 1) return m - h, m + h def mse_loss(x, error): return","# Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size =","= np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1,","j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories =","2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in range(num_subsets):",": j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value","0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma","1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts =","): \"\"\" Take in samples (action, rewards, propensities, etc.) and output lists of","/ len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum(","def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h =","num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum(","sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ):","target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories,","bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, )","= [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory])","edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section","+ confidence) / 2.0, n - 1) return m - h, m +","= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories,","# minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) -",") # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size","= [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory","1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod","denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0","_ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate =","x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories,","np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1,","np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights = (","to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length(","self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate:","= len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights","confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se *","= np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) <","normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps,","replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns,","float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data","j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard","logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier","importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0","importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack(","target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return","edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps","for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2","self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf","= low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] -","j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval","np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] ) -","0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0]","// (num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps -",") target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) )","2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i","Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories,","are of various lengths, the shorter ones are filled with zeros(ones) at the","> 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps -","1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions),","For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities,","raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps,","= 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights","i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 )","in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array(","if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length //","= np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _","using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) #","= np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros]","1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step +","len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /=","- 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end","if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate(","= [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories =","): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias","for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means)","importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts,","1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1)","1], rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length -","list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories","https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) =","weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to estimate confidence","): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros]","1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward =","np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]","self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust","j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T","- control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m,","= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1,","sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs],","= 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends",") else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step +","raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,","j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:,","where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return","low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias +","np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards)","- 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward","np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error","= np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1],","importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]),","j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1)","axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: #","mse error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x","ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL","return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts,","np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page","end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]):","interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in",") @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0)","rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length - 1:","importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def","importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,","direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] )","+ 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value = (","np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values,","j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value -","= int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts,","bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] =","+ variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound","j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def","fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length(","whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights)","+ direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n =","stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps:","denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns,","accoriding to terminals. As the raw trajectories are of various lengths, the shorter","filled with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0])","episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories = []","+ 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step","= np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0]",") estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values),","( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,","estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories =","j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means","where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories)","edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")]",") j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust","actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum(","1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories","num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False)","raw trajectories are of various lengths, the shorter ones are filled with zeros(ones)","target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights","confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se","np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] )","rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust","j_step_bias # minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x)","i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values","import logging import numpy as np import scipy as sp import torch from","from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25","= 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self,","j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum(","equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are of various","logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories","weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step +","np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] =","/= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return(","se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0,","not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends:","import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES",") return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights,",") - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1],","sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards,","importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n","!= mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start =","normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error /","j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower]","torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO)","x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x)","+ 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = (","res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])],","reserved. import itertools import logging import numpy as np import scipy as sp",") def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound =","= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 /","= np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs],","# break trajectories into several subsets to estimate confidence bounds infinite_step_returns = []","denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound","discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0,","np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories,","in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval)","self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL,","len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts,","int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] )","- high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias #","edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if","where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns","subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories","< low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher]","terminals. As the raw trajectories are of various lengths, the shorter ones are","= [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x,","< trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:,","( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence):","estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights","(episodes) accoriding to terminals. As the raw trajectories are of various lengths, the","1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights =","rights reserved. import itertools import logging import numpy as np import scipy as","- j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance","_ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories(","estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward +","action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if","if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break","infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate(","reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights:","to terminals. As the raw trajectories are of various lengths, the shorter ones","def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples","= logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES =","logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities, etc.) and",") Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories,",") # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower =","in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] !=","or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories =","WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute","logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights","int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier","logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories:","* sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m - h,","* 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return","= ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(","Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories(","confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES,","7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(),","# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import itertools","infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means =","= WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length","point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,","import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__)",") return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def","= np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0]","if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1)","len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start =","range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i]","target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return","[] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory])","whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier","reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0,","WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset)","np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights","len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return(","rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(),","= actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps >","base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts,","1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) -","np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:,","= np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1]","np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n -",") num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps >","+ 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = []","importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step","estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2,","j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i","infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) )","logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), )","estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all","1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step","1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return(","class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT =","target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )","standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for","np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, :","\"\"\" Take in samples (action, rewards, propensities, etc.) and output lists of equal-length","Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights,","8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(),",") importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step +","terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 ==","importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma )","len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories","num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i","bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if","trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step","/ logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier",": j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:,","= np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for","low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound","np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights","= np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, :","np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1])","axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0,","= np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse error","weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, )","h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return","trajectories into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets =","rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals.","= ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else:","importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length -","rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns","{\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res =","- 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step +","CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self,","axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action /","= target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights","= len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts =","* estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum(","np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return","transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action,","Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import itertools import","= np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1))","j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance =","__init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, )","np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values =","trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier )","\"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss,","= to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories,","estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using","j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator =","0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights","[] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval =","in samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes)","num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps","episode_end + 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories =","5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id,","== 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into","WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias =","j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) ==","= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 /","calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories","importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights,","= to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories =","lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x,","= len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier","= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights,","0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) )","np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) )","j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error =","weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns,","sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:,","+ 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights","j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, )","whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0]","(action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to","to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories =","Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps,","1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in","/ denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound,","j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return =",") interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange(","= np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals)","= ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x,","EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5,","trajectories (episodes) accoriding to terminals. As the raw trajectories are of various lengths,","CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma","[] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory])","infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps])","to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories,","importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0]","= int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs","WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length =","estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if","interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1)","= len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 +","def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details,","to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod","estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply(","j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply(","= np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 )","estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step,","interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i","high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize","error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _","discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = []","np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start","NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def","- 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end","rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories =","WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5","random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs],","= np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return)","= sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], )","[importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma",") return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se =","logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1)","< 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust","+ 1], rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length","= np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:,","EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9","np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6:","into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int(","n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1","len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier =","np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher =","direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x)","importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate","= np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n","importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step =","x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories =","for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1)","confidence) / 2.0, n - 1) return m - h, m + h","np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size,","1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return =","j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust =",": j_step + 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value","j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take","self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error","CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error,",") importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights","= ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,","importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1)","into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower]","Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length","x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0]","- 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values,","in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values =","np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) #","for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def","[] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in","1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod","0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start","import numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe import",") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length","Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int(","for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length","episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end","action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value))","( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value",": j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if j_step","= len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x +","its affiliates. All rights reserved. import itertools import logging import numpy as np","( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ):",":-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"),",") ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1:","WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps,","min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for","logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50","np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0]","= WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias","= np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights =","1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end +","episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return","interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset]","== 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return","WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for","CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards,","in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids,","all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use","= gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: #","control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se","\"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize(","importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step,","start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in","infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error","[] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in","1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust /","random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards,","= [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not","target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory])","to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT *","[importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset],",") importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier =","actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities,","estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns)","interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions),","BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp:",").swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories,","1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action =","np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1)","( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,","1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward","and its affiliates. All rights reserved. import itertools import logging import numpy as","importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length","= [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,","@staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length =","return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate(","0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps,","trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps","mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x +","else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,","importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) )","/ num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i","50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self,","target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories","= np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions])","importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset],","== mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1","actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if","1) return m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x,","of various lengths, the shorter ones are filled with zeros(ones) at the end.","- 1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action",") ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust","target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2","WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories","WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset","j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error =","np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint,","np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights =","[] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = []","output lists of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories","trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length =","= int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories /","in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate(","ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator:",": j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return","- 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append(","= np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 )","to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories /",") if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step +","num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) #",") ) interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset =","j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x)","gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For","constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)])","se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m -","the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0,","etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals. As the","n - 1) return m - h, m + h def mse_loss(x, error):","trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps","range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x","logging import numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe","actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action","trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x,","= 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage,","error = covariance + j_step_bias.T * j_step_bias # minimize mse error constraint =","the raw trajectories are of various lengths, the shorter ones are filled with","num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7,","num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval","importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0","num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0,","len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence)","1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate =","importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,","j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher]","= np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /=","axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =","[] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES","of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are of","low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] =","various lengths, the shorter ones are filled with zeros(ones) at the end. \"\"\"","the shorter ones are filled with zeros(ones) at the end. \"\"\" num_actions =","Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def","from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class","= to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories,","propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals. As","np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if","[float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length","num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x","= np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0","Facebook, Inc. and its affiliates. All rights reserved. import itertools import logging import","gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) ->",") discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories =","np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse error constraint","if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i *",":-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories","= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset),","j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias","estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, )","for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step,","[] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action,","# decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns","mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = []","direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:,","num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval),","= j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to","normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ):","covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse","bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in","trajectories are of various lengths, the shorter ones are filled with zeros(ones) at","j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories])","/ logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )","1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several","j_step_bias.T * j_step_bias # minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda","-> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions,","= 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma =","in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) )","sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger =","= [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval","episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories","= [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range(","importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories,","zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x","* interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities,","logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =","range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval) )","at the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in","+ 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x]","i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) *","j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to estimate","= to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return (","1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate )","/ 2.0, n - 1) return m - h, m + h def",") importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =","np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else:","num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error","x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]:",") j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod","= [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval =","mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories","normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights","0.0 else: # break trajectories into several subsets to estimate confidence bounds infinite_step_returns","j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, :",") j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights,","1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1)","axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0","len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for","j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,","error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x =","episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends,","), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return","estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights","= j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T *","import itertools import logging import numpy as np import scipy as sp import",") for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort()","@staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in","1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0)","np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) -","sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else:","NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def","importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step","np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length,","int(i * interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset]","weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply(","= self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate)",") importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier =","= np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error","= np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities,","importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories )","CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES =","return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories","1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )","terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if","np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories","weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets )",") infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust =","> high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance","logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories,","high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance +","def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros =","range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions,","importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = (","= num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i *","if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] =","/ denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns,","np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]","sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x","= episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = []",") reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories =","= trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in range(1,","= np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights =","if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1,","reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for","range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action =","wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], )","target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities, etc.) and output","edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps =","# For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities,","action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories","episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories","fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories =","trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts","edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1]","details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values,","variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound -","logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT","j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into","1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step +","j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose","infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator","weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to","axis=1, ) if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step",") infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), )","> 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for","= self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute","weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum(","float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\"","in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start,","compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets","python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import","axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) *","np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error =","len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1","WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories)","scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage","+ 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate","+ 1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends","np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1])","def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds(","else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1],","weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator)","@staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h","* num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size,","bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns))","as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger","= ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,","1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends =","action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 )","sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m - h, m","estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities, etc.) and output lists","ones are filled with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals","covariance + j_step_bias.T * j_step_bias # minimize mse error constraint = {\"type\": \"eq\",","= 0.0 else: # break trajectories into several subsets to estimate confidence bounds","if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1]","normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, )","weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value =","itertools import logging import numpy as np import scipy as sp import torch","= 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate(","high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance","j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories",") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset],","# Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps,","are filled with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals =","return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ):","- np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ),","j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = []","return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ):","actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(),",": j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:,","to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions])","return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator,","* interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] /","= np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate(","j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if j_step <","(num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)])","error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0]","raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error","several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min(","episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends:",") -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 (","np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or","importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights ==","return m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error),","edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps","np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights(","lengths, the shorter ones are filled with zeros(ones) at the end. \"\"\" num_actions","[] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value):","estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length","break trajectories into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets","visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, )"],"string":"[\n \"\\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if\",\n \"j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, )\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns =\",\n \"x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error,\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step\",\n \"axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) *\",\n \"2.0, n - 1) return m - h, m + h def mse_loss(x,\",\n \"where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights\",\n \"axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return\",\n \"in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action\",\n \"Take in samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories\",\n \"estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit\",\n \"As the raw trajectories are of various lengths, the shorter ones are filled\",\n \"reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length(\",\n \"samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding\",\n \"logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES\",\n \"num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards,\",\n \"( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(),\",\n \"* interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights,\",\n \"data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping\",\n \"/= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values,\",\n \"num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights)\",\n \"= {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res\",\n \"np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step\",\n \"= se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m\",\n \"discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, :\",\n \"+ j_step_bias.T * j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\":\",\n \"Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def\",\n \"importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean\",\n \"int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs =\",\n \"for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1\",\n \"): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i\",\n \"num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts),\",\n \"np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0,\",\n \"whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8\",\n \"/ 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in\",\n \"1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:,\",\n \"sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1)\",\n \"/ num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace(\",\n \"infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) )\",\n \"+ 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate\",\n \") estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities\",\n \"target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights =\",\n \"for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point\",\n \"+ 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, :\",\n \"x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for\",\n \"1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] =\",\n \"mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x =\",\n \") bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values)\",\n \"@staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros\",\n \"j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, :\",\n \"All rights reserved. import itertools import logging import numpy as np import scipy\",\n \"numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate\",\n \"25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma):\",\n \"( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,\",\n \"raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator,\",\n \"to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1\",\n \"0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends =\",\n \"rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.)\",\n \"lists of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are\",\n \"axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities,\",\n \"compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns,\",\n \"for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x,\",\n \"trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories\",\n \"action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length(\",\n \"int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets\",\n \"args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return\",\n \"else: # break trajectories into several subsets to estimate confidence bounds infinite_step_returns =\",\n \"import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import\",\n \"mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns,\",\n \"num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories\",\n \"as np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from\",\n \"mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0\",\n \"minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0}\",\n \"actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2:\",\n \"j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] *\",\n \"control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step +\",\n \"1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )\",\n \"affiliates. All rights reserved. import itertools import logging import numpy as np import\",\n \"def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights,\",\n \"in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories =\",\n \"= np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns >\",\n \"m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error), x.T)\",\n \"1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions])\",\n \"def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories\",\n \"episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories =\",\n \"WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower\",\n \"return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x),\",\n \"m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) /\",\n \"+ 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories =\",\n \") x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards,\",\n \"j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length -\",\n \"with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for\",\n \"weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets\",\n \"random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in\",\n \"and output lists of equal-length trajectories (episodes) accoriding to terminals. As the raw\",\n \"episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end +\",\n \"importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack(\",\n \"+ 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step\",\n \"np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1, )\",\n \"estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2\",\n \"np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step\",\n \"= np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 )\",\n \"Inc. and its affiliates. All rights reserved. import itertools import logging import numpy\",\n \"= actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1)\",\n \"* j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x:\",\n \"= to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, )\",\n \"= [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory])\",\n \"whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier\",\n \"- 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0,\",\n \"#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\",\n \"j_step + 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value =\",\n \"abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust,\",\n \"= np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher\",\n \"x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities,\",\n \"np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights,\",\n \"np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier,\",\n \"def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0])\",\n \"constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x,\",\n \"np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in\",\n \"(c) Facebook, Inc. and its affiliates. All rights reserved. import itertools import logging\",\n \"= 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals)\",\n \"import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL =\",\n \"mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards,\",\n \") importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(\",\n \"bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT\",\n \"1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = []\",\n \"): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1))\",\n \"1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1]\",\n \"estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1],\",\n \"trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1\",\n \"num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i +\",\n \"= covariance + j_step_bias.T * j_step_bias # minimize mse error constraint = {\\\"type\\\":\",\n \"shorter ones are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0])\",\n \"* 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts\",\n \"num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1:\",\n \"return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \") = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0]\",\n \"trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights\",\n \"decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns <\",\n \"- 1) return m - h, m + h def mse_loss(x, error): return\",\n \"# Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size =\",\n \"= np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1,\",\n \"j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories =\",\n \"2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in range(num_subsets):\",\n \": j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value\",\n \"0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma\",\n \"1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts =\",\n \"): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and output lists of\",\n \"/ len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum(\",\n \"def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h =\",\n \"num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum(\",\n \"sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ):\",\n \"target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories,\",\n \"bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, )\",\n \"= [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory])\",\n \"edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section\",\n \"+ confidence) / 2.0, n - 1) return m - h, m +\",\n \"= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories,\",\n \"# minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) -\",\n \") # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size\",\n \"= [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory\",\n \"1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod\",\n \"denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0\",\n \"_ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate =\",\n \"x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories,\",\n \"np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1,\",\n \"np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights = (\",\n \"to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length(\",\n \"self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate:\",\n \"= len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights\",\n \"confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se *\",\n \"= np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) <\",\n \"normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps,\",\n \"replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns,\",\n \"float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data\",\n \"j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard\",\n \"logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier\",\n \"importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0\",\n \"importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack(\",\n \"target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return\",\n \"edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps\",\n \"for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2\",\n \"self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf\",\n \"= low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] -\",\n \"j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval\",\n \"np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] ) -\",\n \"0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0]\",\n \"// (num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps -\",\n \") target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) )\",\n \"2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i\",\n \"Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories,\",\n \"are of various lengths, the shorter ones are filled with zeros(ones) at the\",\n \"> 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps -\",\n \"1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions),\",\n \"For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities,\",\n \"raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps,\",\n \"= 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights\",\n \"i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 )\",\n \"in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array(\",\n \"if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length //\",\n \"= np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _\",\n \"using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) #\",\n \"= np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros]\",\n \"1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step +\",\n \"len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /=\",\n \"- 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end\",\n \"if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate(\",\n \"= [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories =\",\n \"): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias\",\n \"for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means)\",\n \"importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts,\",\n \"1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1)\",\n \"1], rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length -\",\n \"list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories\",\n \"https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) =\",\n \"weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to estimate confidence\",\n \"): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros]\",\n \"1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward =\",\n \"np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]\",\n \"self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust\",\n \"j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T\",\n \"- control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m,\",\n \"= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1,\",\n \"sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs],\",\n \"= 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends\",\n \") else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step +\",\n \"raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,\",\n \"j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:,\",\n \"where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return\",\n \"low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias +\",\n \"np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards)\",\n \"- 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward\",\n \"np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error\",\n \"= np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1],\",\n \"importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]),\",\n \"j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1)\",\n \"axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: #\",\n \"mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x\",\n \"ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL\",\n \"return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts,\",\n \"np import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page\",\n \"end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]):\",\n \"interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in\",\n \") @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0)\",\n \"rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length - 1:\",\n \"importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def\",\n \"importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,\",\n \"direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] )\",\n \"+ 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value = (\",\n \"np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values,\",\n \"j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value -\",\n \"= int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts,\",\n \"bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] =\",\n \"+ variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound\",\n \"j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def\",\n \"fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length(\",\n \"whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights)\",\n \"+ direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n =\",\n \"stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps:\",\n \"denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns,\",\n \"accoriding to terminals. As the raw trajectories are of various lengths, the shorter\",\n \"filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0])\",\n \"episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories = []\",\n \"+ 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step\",\n \"= np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0]\",\n \") estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values),\",\n \"( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,\",\n \"estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories =\",\n \"j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means\",\n \"where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories)\",\n \"edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")]\",\n \") j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust\",\n \"actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum(\",\n \"1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories\",\n \"num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False)\",\n \"raw trajectories are of various lengths, the shorter ones are filled with zeros(ones)\",\n \"target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \"confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se\",\n \"np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] )\",\n \"rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust\",\n \"j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x)\",\n \"i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values\",\n \"import logging import numpy as np import scipy as sp import torch from\",\n \"from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25\",\n \"= 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self,\",\n \"j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum(\",\n \"equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are of various\",\n \"logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories\",\n \"weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step +\",\n \"np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] =\",\n \"/= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return(\",\n \"se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0,\",\n \"not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends:\",\n \"import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES\",\n \") return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights,\",\n \") - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1],\",\n \"sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards,\",\n \"importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n\",\n \"!= mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start =\",\n \"normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error /\",\n \"j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower]\",\n \"torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO)\",\n \"x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x)\",\n \"+ 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = (\",\n \"res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])],\",\n \"reserved. import itertools import logging import numpy as np import scipy as sp\",\n \") def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound =\",\n \"= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 /\",\n \"= np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs],\",\n \"# break trajectories into several subsets to estimate confidence bounds infinite_step_returns = []\",\n \"denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound\",\n \"discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0,\",\n \"np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories,\",\n \"in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval)\",\n \"self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL,\",\n \"len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts,\",\n \"int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] )\",\n \"- high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias #\",\n \"edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if\",\n \"where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns\",\n \"subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories\",\n \"< low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher]\",\n \"terminals. As the raw trajectories are of various lengths, the shorter ones are\",\n \"= [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x,\",\n \"< trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:,\",\n \"( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence):\",\n \"estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights\",\n \"(episodes) accoriding to terminals. As the raw trajectories are of various lengths, the\",\n \"1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights =\",\n \"rights reserved. import itertools import logging import numpy as np import scipy as\",\n \"- j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance\",\n \"_ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories(\",\n \"estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward +\",\n \"action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if\",\n \"if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break\",\n \"infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate(\",\n \"reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights:\",\n \"to terminals. As the raw trajectories are of various lengths, the shorter ones\",\n \"def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples\",\n \"= logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES =\",\n \"logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and\",\n \") Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories,\",\n \") # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower =\",\n \"in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] !=\",\n \"or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories =\",\n \"WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute\",\n \"logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights\",\n \"int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier\",\n \"logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories:\",\n \"* sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m - h,\",\n \"* 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return\",\n \"= ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(\",\n \"Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories(\",\n \"confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES,\",\n \"7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(),\",\n \"# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import itertools\",\n \"infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means =\",\n \"= WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length\",\n \"point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,\",\n \"import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__)\",\n \") return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def\",\n \"= np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0]\",\n \"if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1)\",\n \"len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start =\",\n \"range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i]\",\n \"target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return\",\n \"[] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory])\",\n \"whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier\",\n \"reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0,\",\n \"WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset)\",\n \"np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights\",\n \"len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return(\",\n \"rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(),\",\n \"= actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps >\",\n \"base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts,\",\n \"1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) -\",\n \"np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:,\",\n \"= np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1]\",\n \"np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n -\",\n \") num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps >\",\n \"+ 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories = []\",\n \"importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step\",\n \"estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2,\",\n \"j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i\",\n \"infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) )\",\n \"logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), )\",\n \"estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all\",\n \"1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step\",\n \"1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return(\",\n \"class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT =\",\n \"target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )\",\n \"standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for\",\n \"np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, :\",\n \"\\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and output lists of equal-length\",\n \"Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights,\",\n \"8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(),\",\n \") importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step +\",\n \"terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 ==\",\n \"importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma )\",\n \"len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories\",\n \"num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i\",\n \"bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if\",\n \"trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step\",\n \"/ logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier\",\n \": j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:,\",\n \"= np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for\",\n \"low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound\",\n \"np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights\",\n \"= np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, :\",\n \"np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1])\",\n \"axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0,\",\n \"= np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse error\",\n \"weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, )\",\n \"h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return\",\n \"trajectories into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets =\",\n \"rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals.\",\n \"= ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else:\",\n \"importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length -\",\n \"rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns\",\n \"{\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res =\",\n \"- 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step +\",\n \"CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self,\",\n \"axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action /\",\n \"= target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \"= len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts =\",\n \"* estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum(\",\n \"np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return\",\n \"transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action,\",\n \"Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import itertools import\",\n \"= np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1))\",\n \"j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance =\",\n \"__init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, )\",\n \"np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values =\",\n \"trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier )\",\n \"\\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss,\",\n \"= to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories,\",\n \"estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using\",\n \"j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator =\",\n \"0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights\",\n \"[] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval =\",\n \"in samples (action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes)\",\n \"num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps\",\n \"episode_end + 1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories =\",\n \"5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id,\",\n \"== 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into\",\n \"WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias =\",\n \"j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) ==\",\n \"= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 /\",\n \"calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories\",\n \"importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights,\",\n \"= to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories =\",\n \"lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x,\",\n \"= len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier\",\n \"= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights,\",\n \"0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) )\",\n \"np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) )\",\n \"j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error =\",\n \"weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns,\",\n \"sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:,\",\n \"+ 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights\",\n \"j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, )\",\n \"whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0]\",\n \"(action, rewards, propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to\",\n \"to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories =\",\n \"Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps,\",\n \"1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in\",\n \"/ denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound,\",\n \"j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return =\",\n \") interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange(\",\n \"= np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals)\",\n \"= ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x,\",\n \"EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5,\",\n \"trajectories (episodes) accoriding to terminals. As the raw trajectories are of various lengths,\",\n \"CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma\",\n \"[] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory])\",\n \"infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias = np.zeros([num_j_steps])\",\n \"to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories,\",\n \"importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0]\",\n \"= int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs\",\n \"WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length =\",\n \"estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if\",\n \"interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1)\",\n \"= len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 +\",\n \"def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details,\",\n \"to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod\",\n \"estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply(\",\n \"j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply(\",\n \"= np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 )\",\n \"estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step,\",\n \"interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i\",\n \"high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize\",\n \"error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _\",\n \"discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = []\",\n \"np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start\",\n \"NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def\",\n \"- 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end\",\n \"rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories =\",\n \"WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5\",\n \"random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs],\",\n \"= np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return)\",\n \"= sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], )\",\n \"[importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma\",\n \") return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se =\",\n \"logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1)\",\n \"< 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust\",\n \"+ 1], rewards[:, : j_step + 1]), axis=1, ) if j_step < trajectory_length\",\n \"= np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:,\",\n \"EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9\",\n \"np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6:\",\n \"into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int(\",\n \"n = len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1\",\n \"len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier =\",\n \"np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher =\",\n \"direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x)\",\n \"importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate\",\n \"= np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n\",\n \"importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step =\",\n \"x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories =\",\n \"for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1)\",\n \"confidence) / 2.0, n - 1) return m - h, m + h\",\n \"np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size,\",\n \"1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return =\",\n \"j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust =\",\n \": j_step + 1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value\",\n \"j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take\",\n \"self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error\",\n \"CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error,\",\n \") importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights\",\n \"= ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,\",\n \"importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1)\",\n \"into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower]\",\n \"Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length\",\n \"x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0]\",\n \"- 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values,\",\n \"in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values =\",\n \"np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) #\",\n \"for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def\",\n \"[] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in\",\n \"1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod\",\n \"0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = [] episode_start\",\n \"import numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe import\",\n \") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length\",\n \"Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int(\",\n \"for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length\",\n \"episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end\",\n \"action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value))\",\n \"( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value\",\n \": j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if j_step\",\n \"= len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x +\",\n \"its affiliates. All rights reserved. import itertools import logging import numpy as np\",\n \"( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ):\",\n \":-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"),\",\n \") ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1:\",\n \"WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps,\",\n \"min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for\",\n \"logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50\",\n \"np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0]\",\n \"= WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias\",\n \"= np.arange( int(i * interval), int((i + 1) * interval) ) importance_weights =\",\n \"1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end +\",\n \"episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return\",\n \"interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset]\",\n \"== 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return\",\n \"WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for\",\n \"CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards,\",\n \"in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids,\",\n \"all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use\",\n \"= gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: #\",\n \"control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se\",\n \"\\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize(\",\n \"importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step,\",\n \"start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in\",\n \"infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error\",\n \"[] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in\",\n \"1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust /\",\n \"random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards,\",\n \"= [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not\",\n \"target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory])\",\n \"to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT *\",\n \"[importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset],\",\n \") importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier =\",\n \"actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities,\",\n \"estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns)\",\n \"interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions),\",\n \"BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp:\",\n \").swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories,\",\n \"1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action =\",\n \"np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1)\",\n \"( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,\",\n \"1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward\",\n \"and its affiliates. All rights reserved. import itertools import logging import numpy as\",\n \"importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length\",\n \"= [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,\",\n \"@staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length =\",\n \"return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate(\",\n \"0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps,\",\n \"trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps\",\n \"mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x +\",\n \"else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,\",\n \"importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) )\",\n \"/ num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i\",\n \"50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self,\",\n \"target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories\",\n \"= np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions])\",\n \"importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset],\",\n \"== mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1\",\n \"actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if\",\n \"1) return m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x,\",\n \"of various lengths, the shorter ones are filled with zeros(ones) at the end.\",\n \"- 1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action\",\n \") ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust\",\n \"target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2\",\n \"WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories\",\n \"WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset\",\n \"j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error =\",\n \"np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint,\",\n \"np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights =\",\n \"[] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = []\",\n \"output lists of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories\",\n \"trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length =\",\n \"= int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories /\",\n \"in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate(\",\n \"ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator:\",\n \": j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return\",\n \"- 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append(\",\n \"= np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 )\",\n \"to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories /\",\n \") if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step +\",\n \"num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) #\",\n \") ) interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset =\",\n \"j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x)\",\n \"gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For\",\n \"constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)])\",\n \"se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m -\",\n \"the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0,\",\n \"etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals. As the\",\n \"n - 1) return m - h, m + h def mse_loss(x, error):\",\n \"trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps\",\n \"range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x\",\n \"logging import numpy as np import scipy as sp import torch from ml.rl.evaluation.cpe\",\n \"actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action\",\n \"trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x,\",\n \"= 0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage,\",\n \"error = covariance + j_step_bias.T * j_step_bias # minimize mse error constraint =\",\n \"the raw trajectories are of various lengths, the shorter ones are filled with\",\n \"num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7,\",\n \"num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval\",\n \"importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0\",\n \"num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0,\",\n \"len(x) m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence)\",\n \"1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate =\",\n \"importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,\",\n \"j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher]\",\n \"= np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /=\",\n \"axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =\",\n \"[] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES\",\n \"of equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories are of\",\n \"low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] =\",\n \"various lengths, the shorter ones are filled with zeros(ones) at the end. \\\"\\\"\\\"\",\n \"the shorter ones are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions =\",\n \"Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def\",\n \"from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class\",\n \"= to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories,\",\n \"propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding to terminals. As\",\n \"np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if\",\n \"[float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length\",\n \"num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x\",\n \"= np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0\",\n \"Facebook, Inc. and its affiliates. All rights reserved. import itertools import logging import\",\n \"gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) ->\",\n \") discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories =\",\n \"np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse error constraint\",\n \"if num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i *\",\n \":-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories\",\n \"= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset),\",\n \"j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias\",\n \"estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, )\",\n \"for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step,\",\n \"[] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action,\",\n \"# decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns\",\n \"mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = []\",\n \"direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:,\",\n \"num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval),\",\n \"= j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to\",\n \"normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ):\",\n \"covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize mse\",\n \"bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in\",\n \"trajectories are of various lengths, the shorter ones are filled with zeros(ones) at\",\n \"j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories])\",\n \"/ logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )\",\n \"1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several\",\n \"j_step_bias.T * j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda\",\n \"-> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions,\",\n \"= 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma =\",\n \"in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) )\",\n \"sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger =\",\n \"= [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval\",\n \"episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories\",\n \"= [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range(\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories,\",\n \"zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x\",\n \"* interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities,\",\n \"logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =\",\n \"range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) * interval) )\",\n \"at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in\",\n \"+ 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x]\",\n \"i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1) *\",\n \"j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets to estimate\",\n \"= to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return (\",\n \"1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate )\",\n \"/ 2.0, n - 1) return m - h, m + h def\",\n \") importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =\",\n \"np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else:\",\n \"num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error\",\n \"x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]:\",\n \") j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod\",\n \"= [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval =\",\n \"mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories\",\n \"normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights\",\n \"0.0 else: # break trajectories into several subsets to estimate confidence bounds infinite_step_returns\",\n \"j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, :\",\n \") j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights,\",\n \"1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1)\",\n \"axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0\",\n \"len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for\",\n \"j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,\",\n \"error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x =\",\n \"episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends,\",\n \"), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return\",\n \"estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights\",\n \"= j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T *\",\n \"import itertools import logging import numpy as np import scipy as sp import\",\n \") for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort()\",\n \"@staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in\",\n \"1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0)\",\n \"np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) -\",\n \"sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else:\",\n \"NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def\",\n \"importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step\",\n \"np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length,\",\n \"int(i * interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset]\",\n \"weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply(\",\n \"= self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate)\",\n \") importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier =\",\n \"= np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error\",\n \"= np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities,\",\n \"importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories )\",\n \"CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES =\",\n \"return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories\",\n \"1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )\",\n \"terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if\",\n \"np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories\",\n \"weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets )\",\n \") infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust =\",\n \"> high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance\",\n \"logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories,\",\n \"high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance +\",\n \"def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros =\",\n \"range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions,\",\n \"importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = (\",\n \"= num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i *\",\n \"if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] =\",\n \"/ denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns,\",\n \"np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]\",\n \"sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x\",\n \"= episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = []\",\n \") reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories =\",\n \"= trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i in range(1,\",\n \"= np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights =\",\n \"if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1,\",\n \"reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for\",\n \"range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action =\",\n \"wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], )\",\n \"target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and output\",\n \"edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps =\",\n \"# For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities,\",\n \"action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories\",\n \"episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories\",\n \"fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories =\",\n \"trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts\",\n \"edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1]\",\n \"details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values,\",\n \"variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound -\",\n \"logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT\",\n \"j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into\",\n \"1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step +\",\n \"j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose\",\n \"infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator\",\n \"weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to\",\n \"axis=1, ) if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step\",\n \") infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), )\",\n \"> 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for\",\n \"= self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute\",\n \"weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum(\",\n \"float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\"\",\n \"in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start,\",\n \"compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets\",\n \"python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import\",\n \"axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) *\",\n \"np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error =\",\n \"len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1\",\n \"WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories)\",\n \"scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage\",\n \"+ 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate\",\n \"+ 1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends\",\n \"np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1])\",\n \"def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds(\",\n \"else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1],\",\n \"weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator)\",\n \"@staticmethod def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h\",\n \"* num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size,\",\n \"bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns))\",\n \"as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger\",\n \"= ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,\",\n \"1, 0]: terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends =\",\n \"action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 )\",\n \"sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m - h, m\",\n \"estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and output lists\",\n \"ones are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals\",\n \"covariance + j_step_bias.T * j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\",\",\n \"= 0.0 else: # break trajectories into several subsets to estimate confidence bounds\",\n \"if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1]\",\n \"normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, )\",\n \"weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value =\",\n \"itertools import logging import numpy as np import scipy as sp import torch\",\n \"= 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def estimate(\",\n \"high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance\",\n \"j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories\",\n \") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset],\",\n \"# Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps,\",\n \"are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals =\",\n \"return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ):\",\n \"- np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1], ),\",\n \"j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = []\",\n \"return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ):\",\n \"actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(),\",\n \": j_step + 1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:,\",\n \"to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions])\",\n \"return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator,\",\n \"* interval), int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] /\",\n \"= np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate(\",\n \"j_step + 1], rewards[:, : j_step + 1]), axis=1, ) if j_step <\",\n \"(num_j_steps - 1) j_steps.extend([i * interval for i in range(1, num_j_steps - 1)])\",\n \"error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0]\",\n \"raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error\",\n \"several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets = int( min(\",\n \"episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends:\",\n \") -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 (\",\n \"np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or\",\n \"importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights ==\",\n \"return m - h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error),\",\n \"edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps\",\n \"np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights(\",\n \"lengths, the shorter ones are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions\",\n \"[] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value):\",\n \"estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length\",\n \"break trajectories into several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets\",\n \"visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, )\"\n]","isConversational":false}}},{"rowIdx":3480,"cells":{"ngram":{"kind":"list like","value":["array to its original configuration and return it. \"\"\" return self.original def shuffle(self)","coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last","nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\"","Modified time: 2019-08-03 10:53:15 import copy import random from typing import List class","List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums)","# -*- coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30","# @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy","List[int]: \"\"\" Returns a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_) return self.shuffle_","List[int]: \"\"\" Resets the array to its original configuration and return it. \"\"\"","@Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time:","copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array to its original configuration","from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums","typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original","= nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array","return it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns a random","nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array to","configuration and return it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns","random from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ =","# @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿","@Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import","and return it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns a","def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of the array. \"\"\"","__init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]:","self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the","time: 2019-08-03 10:53:15 import copy import random from typing import List class Solution:","def reset(self) -> List[int]: \"\"\" Resets the array to its original configuration and","return self.original def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of the","Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self)","Resets the array to its original configuration and return it. \"\"\" return self.original","import copy import random from typing import List class Solution: def __init__(self, nums:","\"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of","shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_)","10:53:15 import copy import random from typing import List class Solution: def __init__(self,","import random from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_","2019-08-03 10:53:15 import copy import random from typing import List class Solution: def","its original configuration and return it. \"\"\" return self.original def shuffle(self) -> List[int]:","import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original =","it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling","self.original def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of the array.","List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets","\"\"\" Resets the array to its original configuration and return it. \"\"\" return","何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random from typing","= copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array to its original","-*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by:","2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15","the array to its original configuration and return it. \"\"\" return self.original def","utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified","class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def","10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import","# @Last Modified time: 2019-08-03 10:53:15 import copy import random from typing import","original configuration and return it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\"","-> List[int]: \"\"\" Returns a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_) return","Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03","-*- coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 #","# @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified","reset(self) -> List[int]: \"\"\" Resets the array to its original configuration and return","Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random","self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array to its","-> List[int]: \"\"\" Resets the array to its original configuration and return it.","@Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 #","何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last","to its original configuration and return it. \"\"\" return self.original def shuffle(self) ->","def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) ->","by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random from","@Last Modified time: 2019-08-03 10:53:15 import copy import random from typing import List","copy import random from typing import List class Solution: def __init__(self, nums: List[int]):"],"string":"[\n \"array to its original configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self)\",\n \"coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last\",\n \"nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\"\",\n \"Modified time: 2019-08-03 10:53:15 import copy import random from typing import List class\",\n \"List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums)\",\n \"# -*- coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30\",\n \"# @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy\",\n \"List[int]: \\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_) return self.shuffle_\",\n \"List[int]: \\\"\\\"\\\" Resets the array to its original configuration and return it. \\\"\\\"\\\"\",\n \"@Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time:\",\n \"copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its original configuration\",\n \"from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums\",\n \"typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original\",\n \"= nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array\",\n \"return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random\",\n \"nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to\",\n \"configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns\",\n \"random from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ =\",\n \"# @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿\",\n \"@Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import\",\n \"and return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a\",\n \"def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\"\",\n \"__init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]:\",\n \"self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the\",\n \"time: 2019-08-03 10:53:15 import copy import random from typing import List class Solution:\",\n \"def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its original configuration and\",\n \"return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of the\",\n \"Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self)\",\n \"Resets the array to its original configuration and return it. \\\"\\\"\\\" return self.original\",\n \"import copy import random from typing import List class Solution: def __init__(self, nums:\",\n \"\\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of\",\n \"shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_)\",\n \"10:53:15 import copy import random from typing import List class Solution: def __init__(self,\",\n \"import random from typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_\",\n \"2019-08-03 10:53:15 import copy import random from typing import List class Solution: def\",\n \"its original configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]:\",\n \"import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original =\",\n \"it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling\",\n \"self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of the array.\",\n \"List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets\",\n \"\\\"\\\"\\\" Resets the array to its original configuration and return it. \\\"\\\"\\\" return\",\n \"何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random from typing\",\n \"= copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its original\",\n \"-*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by:\",\n \"2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15\",\n \"the array to its original configuration and return it. \\\"\\\"\\\" return self.original def\",\n \"utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified\",\n \"class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def\",\n \"10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import\",\n \"# @Last Modified time: 2019-08-03 10:53:15 import copy import random from typing import\",\n \"original configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\"\",\n \"-> List[int]: \\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_) return\",\n \"Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified time: 2019-08-03\",\n \"-*- coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 #\",\n \"# @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified\",\n \"reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its original configuration and return\",\n \"Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random\",\n \"self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its\",\n \"-> List[int]: \\\"\\\"\\\" Resets the array to its original configuration and return it.\",\n \"@Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 #\",\n \"何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last\",\n \"to its original configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self) ->\",\n \"def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) ->\",\n \"by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import random from\",\n \"@Last Modified time: 2019-08-03 10:53:15 import copy import random from typing import List\",\n \"copy import random from typing import List class Solution: def __init__(self, nums: List[int]):\"\n]","isConversational":false}}},{"rowIdx":3481,"cells":{"ngram":{"kind":"list like","value":["- feature x column y - feature y column Returns ---------- phi coefficient","features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals,","clusters the features in X according to dist_matrix, and selects a feature from","def _get_support_mask(self): \"\"\" Get the boolean mask indicating which features are selected Returns","highest chi2 score versus y. The attribute self.n_features_ represents the number of features","value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1","\"\"\" Get the boolean mask indicating which features are selected Returns ---------- mask","to dist_matrix, and selects a feature from each cluster with the highest chi2","---------- k - the k parameter for the SelectKBest features function threshold -","_hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created","contains the indexes of features belonging to the cluster \"\"\" # array of","X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold =","with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1","feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method)","Get the boolean mask indicating which features are selected Returns ---------- mask -","1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat","- clustering threshold for the Hierarchial clustering random_state - random state for the","with the highest chi2 score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None,","np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\"","feature y column Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y)","of shape [# input features] An element is True iff its corresponding feature","the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters =","fitted by verifying the presence of fitted attributes (ending with a trailing #","between each two features in X, each value is 1-phi_correlation \"\"\" X_df =","clustering threshold for the Hierarchial clustering random_state - random state for the RandomForestClassifier.","BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest,","mask indicating which features are selected Returns ---------- mask - boolean array of","represents the number of features selected (=number of clusters) The attribute self.selected_features_ is","selected for retention. \"\"\" # Checks if the estimator is fitted by verifying","each two features in X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X)","of features selected (=number of clusters) The attribute self.selected_features_ is a list of","x, y): \"\"\" Calculates phi coefficient between features Parameters ---------- x - feature","self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals","np import sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster","self.n_features_ represents the number of features selected (=number of clusters) The attribute self.selected_features_","attribute self.selected_features_ is a list of indexes that correspond to the selected features","chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer","is fitted by verifying the presence of fitted attributes (ending with a trailing","sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from","hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier","as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import","---------- a list of lists, each list represents a cluster and contains the","clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted","= ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return","retention. \"\"\" # Checks if the estimator is fitted by verifying the presence","mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k","def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x,","/ n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each","indexes of features belonging to the cluster \"\"\" # array of len(X) -","= self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals)","the SelectKBest features function threshold - clustering threshold for the Hierarchial clustering random_state","is the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold,","\"\"\" Calculate distance matrix between each two features in X, each value is","- feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)),","each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix =","import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from","_phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features Parameters ---------- x -","An element is True iff its corresponding feature is selected for retention. \"\"\"","x column y - feature y column Returns ---------- phi coefficient value \"\"\"","is selected for retention. \"\"\" # Checks if the estimator is fitted by","mask - boolean array of shape [# input features] An element is True","column y - feature y column Returns ---------- phi coefficient value \"\"\" confusion_matrix","with the highest chi2 score versus y. The attribute self.n_features_ represents the number","highest chi2 score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix","in X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef)","threshold for the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult","hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns","- X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage =","selection pipeline Parameters ---------- k - the k parameter for the SelectKBest features","features in X according to dist_matrix, and selects a feature from each cluster","chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self,","import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import","otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ),","SelectKBest features function threshold - clustering threshold for the Hierarchial clustering random_state -","feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage):","distance matrix between each two features in X, each value is 1-phi_correlation \"\"\"","and selects a feature from each cluster with the highest chi2 score of","= clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean mask","fit(self, X, y): \"\"\" Clusters the features (X columns) using self.dist_matrix and self.threshold,","to the cluster \"\"\" # array of len(X) - array[i] is the cluster","- feature y column Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x,","attributes (ending with a trailing # underscore) and otherwise raises a NotFittedError with","the estimator is fitted by verifying the presence of fitted attributes (ending with","if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ =","y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def","sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ =","is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 -","cluster \"\"\" # array of len(X) - array[i] is the cluster number to","the number of features selected (=number of clusters) The attribute self.selected_features_ is a","a cluster and contains the indexes of features belonging to the cluster \"\"\"","SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features in X according to","Parameters ---------- x - feature x column y - feature y column Returns","clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ----------","BaseEstimator): \"\"\" A transformer that clusters the features in X according to dist_matrix,","cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def","return self def _get_support_mask(self): \"\"\" Get the boolean mask indicating which features are","True iff its corresponding feature is selected for retention. \"\"\" # Checks if","selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)),","self.threshold, and selects a feature from each cluster with the highest chi2 score","mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold,","phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n =","for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self):","cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features (X","for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline","import numpy as np import sklearn import pandas as pd import scipy.spatial.distance as","features in X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat =","\"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix,","SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that","cluster with the highest chi2 score of X[feature] versus y \"\"\" def __init__(self,","# array of len(X) - array[i] is the cluster number to which sample","indicating which features are selected Returns ---------- mask - boolean array of shape","_get_support_mask(self): \"\"\" Get the boolean mask indicating which features are selected Returns ----------","import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import","and contains the indexes of features belonging to the cluster \"\"\" # array","coefficient between features Parameters ---------- x - feature x column y - feature","dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature","import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from","pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2,","ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage","array of len(X) - array[i] is the cluster number to which sample i","enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features","return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ----------","y - feature y column Returns ---------- phi coefficient value \"\"\" confusion_matrix =","created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents","cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx","underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask =","correspond to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage()","---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n","\"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self,","= hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id,","method=method) Returns ---------- a list of lists, each list represents a cluster and","chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ =","- the k parameter for the SelectKBest features function threshold - clustering threshold","X, y): \"\"\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and","to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {}","Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects a feature","sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class","boolean array of shape [# input features] An element is True iff its","chi2 score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix =","sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy","_corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform","verifying the presence of fitted attributes (ending with a trailing # underscore) and","coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum()","the features in X according to dist_matrix, and selects a feature from each","value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr","is True iff its corresponding feature is selected for retention. \"\"\" # Checks","self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) #","chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for","cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids):","Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns","y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return","Calculate distance matrix between each two features in X, each value is 1-phi_correlation","sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection","using self.dist_matrix and self.threshold, and selects a feature from each cluster with the","features belonging to the cluster \"\"\" # array of len(X) - array[i] is","value: 0 Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize',","selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean mask indicating which features","= X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f},","method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage","return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features (X columns) using","threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates","from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline","= sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_","linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals =","\"\"\" A transformer that clusters the features in X according to dist_matrix, and","= confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\"","X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix =","random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature","and self.threshold, and selects a feature from each cluster with the highest chi2","= Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return pipeline","dist_matrix, and selects a feature from each cluster with the highest chi2 score","its corresponding feature is selected for retention. \"\"\" # Checks if the estimator","from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\"","from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from","self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates phi","function threshold - clustering threshold for the Hierarchial clustering random_state - random state","selects a feature from each cluster with the highest chi2 score of X[feature]","Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),","selected (=number of clusters) The attribute self.selected_features_ is a list of indexes that","import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import","Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features in X","self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return","shape [# input features] An element is True iff its corresponding feature is","def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram","[chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def","scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base","list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features (X columns) using self.dist_matrix","transformer that clusters the features in X according to dist_matrix, and selects a","cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters","number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx =","[]).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features (X columns)","of clusters) The attribute self.selected_features_ is a list of indexes that correspond to","= {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self,","given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask","def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two features in X,","that clusters the features in X according to dist_matrix, and selects a feature","\"\"\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects a","and selects a feature from each cluster with the highest chi2 score versus","list of indexes that correspond to the selected features \"\"\" if not self.dist_matrix:","array of shape [# input features] An element is True iff its corresponding","\"\"\" Creates feature selection pipeline Parameters ---------- k - the k parameter for","Creates feature selection pipeline Parameters ---------- k - the k parameter for the","feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self,","def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features Parameters ---------- x","\"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __","sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A","The attribute self.selected_features_ is a list of indexes that correspond to the selected","as np import sklearn import pandas as pd import scipy.spatial.distance as ssd from","the cluster \"\"\" # array of len(X) - array[i] is the cluster number","according to dist_matrix, and selects a feature from each cluster with the highest","NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] =","trailing # underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self)","for the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value:","(ending with a trailing # underscore) and otherwise raises a NotFittedError with the","the boolean mask indicating which features are selected Returns ---------- mask - boolean","list of lists, each list represents a cluster and contains the indexes of","random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k - the k parameter","a feature from each cluster with the highest chi2 score versus y. The","features are selected Returns ---------- mask - boolean array of shape [# input","a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_]","of indexes that correspond to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X)","feature selection pipeline Parameters ---------- k - the k parameter for the SelectKBest","a list of lists, each list represents a cluster and contains the indexes","__init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y):","---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list","X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def","dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\"","iff its corresponding feature is selected for retention. \"\"\" # Checks if the","is a list of indexes that correspond to the selected features \"\"\" if","the highest chi2 score versus y. The attribute self.n_features_ represents the number of","cluster and contains the indexes of features belonging to the cluster \"\"\" #","---------- pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best',","import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features in","selects a feature from each cluster with the highest chi2 score versus y.","Checks if the estimator is fitted by verifying the presence of fitted attributes","\"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))])","SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters","that correspond to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage =","parameter for the SelectKBest features function threshold - clustering threshold for the Hierarchial","the indexes of features belonging to the cluster \"\"\" # array of len(X)","each list represents a cluster and contains the indexes of features belonging to","= hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters","threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k - the k","are selected Returns ---------- mask - boolean array of shape [# input features]","\"\"\" # Checks if the estimator is fitted by verifying the presence of","- linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists,","by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents a","and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_,","estimator is fitted by verifying the presence of fitted attributes (ending with a","fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_","[# input features] An element is True iff its corresponding feature is selected","k parameter for the SelectKBest features function threshold - clustering threshold for the","the highest chi2 score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1):","import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer","each cluster with the highest chi2 score versus y. The attribute self.n_features_ represents","hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ----------","chi2 score versus y. The attribute self.n_features_ represents the number of features selected","idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\"","the k parameter for the SelectKBest features function threshold - clustering threshold for","sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx,","= X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed","class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features in X according","a trailing # underscore) and otherwise raises a NotFittedError with the given message.","from each cluster with the highest chi2 score versus y. The attribute self.n_features_","cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\"","of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold","# underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask","feature from each cluster with the highest chi2 score of X[feature] versus y","self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters","features function threshold - clustering threshold for the Hierarchial clustering random_state - random","from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from","(X columns) using self.dist_matrix and self.threshold, and selects a feature from each cluster","from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from","criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values())","def fit(self, X, y): \"\"\" Clusters the features (X columns) using self.dist_matrix and","CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator):","= 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline","for retention. \"\"\" # Checks if the estimator is fitted by verifying the","in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the","pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf',","Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method)","self.dist_matrix and self.threshold, and selects a feature from each cluster with the highest","pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in","boolean mask indicating which features are selected Returns ---------- mask - boolean array","y. The attribute self.n_features_ represents the number of features selected (=number of clusters)","ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator","a list of indexes that correspond to the selected features \"\"\" if not","random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline","dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list","of lists, each list represents a cluster and contains the indexes of features","(=number of clusters) The attribute self.selected_features_ is a list of indexes that correspond","= np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix","the features (X columns) using self.dist_matrix and self.threshold, and selects a feature from","hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents a cluster","import sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import","y): \"\"\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects","clustering random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns ----------","between features Parameters ---------- x - feature x column y - feature y","= threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features Parameters","n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X):","= self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes","the presence of fitted attributes (ending with a trailing # underscore) and otherwise","from each cluster with the highest chi2 score of X[feature] versus y \"\"\"","return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two features","feature from each cluster with the highest chi2 score versus y. The attribute","from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin","the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance')","k - the k parameter for the SelectKBest features function threshold - clustering","numpy as np import sklearn import pandas as pd import scipy.spatial.distance as ssd","number of features selected (=number of clusters) The attribute self.selected_features_ is a list","list represents a cluster and contains the indexes of features belonging to the","if the estimator is fitted by verifying the presence of fitted attributes (ending","presence of fitted attributes (ending with a trailing # underscore) and otherwise raises","X): \"\"\" Calculate distance matrix between each two features in X, each value","two features in X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat","X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed =","features selected (=number of clusters) The attribute self.selected_features_ is a list of indexes","x - feature x column y - feature y column Returns ---------- phi","of features belonging to the cluster \"\"\" # array of len(X) - array[i]","mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection","X according to dist_matrix, and selects a feature from each cluster with the","def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k -","print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean mask indicating which","the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \"\"\"","n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two","pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency","which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for","scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text","attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ =","---------- mask - boolean array of shape [# input features] An element is","linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage","not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X,","len(X) - array[i] is the cluster number to which sample i belongs cluster_ids","for the SelectKBest features function threshold - clustering threshold for the Hierarchial clustering","features Parameters ---------- x - feature x column y - feature y column","corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance","feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster',","each cluster with the highest chi2 score of X[feature] versus y \"\"\" def","pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats","__ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1]","matrix between each two features in X, each value is 1-phi_correlation \"\"\" X_df","- boolean array of shape [# input features] An element is True iff","X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix","array[i] is the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage,","raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool)","in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get","score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix","= chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def","columns) using self.dist_matrix and self.threshold, and selects a feature from each cluster with","clusters) The attribute self.selected_features_ is a list of indexes that correspond to the","the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value: 0","represents a cluster and contains the indexes of features belonging to the cluster","clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean mask indicating","by verifying the presence of fitted attributes (ending with a trailing # underscore)","scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble","1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage","= [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self","score versus y. The attribute self.n_features_ represents the number of features selected (=number","attribute self.n_features_ represents the number of features selected (=number of clusters) The attribute","self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y)","y column Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2","_calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two features in X, each","sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k,","def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\"","features (X columns) using self.dist_matrix and self.threshold, and selects a feature from each","RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import","self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean","with a trailing # underscore) and otherwise raises a NotFittedError with the given","self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return","features] An element is True iff its corresponding feature is selected for retention.","get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k - the","linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by","linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each","from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the","pipeline Parameters ---------- k - the k parameter for the SelectKBest features function","= pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster","of len(X) - array[i] is the cluster number to which sample i belongs","confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2","method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical","threshold - clustering threshold for the Hierarchial clustering random_state - random state for","0 Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False,","linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering","Calculates phi coefficient between features Parameters ---------- x - feature x column y","import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin,","pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return","\"\"\" # array of len(X) - array[i] is the cluster number to which","phi coefficient between features Parameters ---------- x - feature x column y -","selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage)","in X according to dist_matrix, and selects a feature from each cluster with","Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a","threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features Parameters ----------","\"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed","= dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient","as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import","= 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'):","dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between","Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline =","\"\"\" Calculates phi coefficient between features Parameters ---------- x - feature x column","self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def","np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between","self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_","---------- x - feature x column y - feature y column Returns ----------","confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate","corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two features in","chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr","= pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 /","X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}')","self def _get_support_mask(self): \"\"\" Get the boolean mask indicating which features are selected","return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage -","sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features","versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold","belonging to the cluster \"\"\" # array of len(X) - array[i] is the","{} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X,","Returns ---------- mask - boolean array of shape [# input features] An element","which features are selected Returns ---------- mask - boolean array of shape [#","- random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline -","indexes that correspond to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage","pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n)","belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in","column Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 =","to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters","RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline","element is True iff its corresponding feature is selected for retention. \"\"\" #","\"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr =","selected Returns ---------- mask - boolean array of shape [# input features] An","- array[i] is the cluster number to which sample i belongs cluster_ids =","pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix","state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection","the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return","# fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters]","input features] An element is True iff its corresponding feature is selected for","self.selected_features_ is a list of indexes that correspond to the selected features \"\"\"","= pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix)","= feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self,","self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features","X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix,","corresponding feature is selected for retention. \"\"\" # Checks if the estimator is","of fitted attributes (ending with a trailing # underscore) and otherwise raises a","import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline","A transformer that clusters the features in X according to dist_matrix, and selects","for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y):","Returns ---------- a list of lists, each list represents a cluster and contains","message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def","# Checks if the estimator is fitted by verifying the presence of fitted","The attribute self.n_features_ represents the number of features selected (=number of clusters) The","clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the","hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx)","y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax()","a feature from each cluster with the highest chi2 score of X[feature] versus","), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates","versus y. The attribute self.n_features_ represents the number of features selected (=number of","1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters","= np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0):","feature is selected for retention. \"\"\" # Checks if the estimator is fitted","Parameters ---------- k - the k parameter for the SelectKBest features function threshold","lists, each list represents a cluster and contains the indexes of features belonging","cluster with the highest chi2 score versus y. The attribute self.n_features_ represents the","y): \"\"\" Calculates phi coefficient between features Parameters ---------- x - feature x","linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of","feature x column y - feature y column Returns ---------- phi coefficient value","Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0]","fitted attributes (ending with a trailing # underscore) and otherwise raises a NotFittedError","i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id"],"string":"[\n \"- feature x column y - feature y column Returns ---------- phi coefficient\",\n \"features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals,\",\n \"clusters the features in X according to dist_matrix, and selects a feature from\",\n \"def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features are selected Returns\",\n \"highest chi2 score versus y. The attribute self.n_features_ represents the number of features\",\n \"value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1\",\n \"\\\"\\\"\\\" Get the boolean mask indicating which features are selected Returns ---------- mask\",\n \"to dist_matrix, and selects a feature from each cluster with the highest chi2\",\n \"---------- k - the k parameter for the SelectKBest features function threshold -\",\n \"_hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created\",\n \"contains the indexes of features belonging to the cluster \\\"\\\"\\\" # array of\",\n \"X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold =\",\n \"with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1\",\n \"feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method)\",\n \"Get the boolean mask indicating which features are selected Returns ---------- mask -\",\n \"1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat\",\n \"- clustering threshold for the Hierarchial clustering random_state - random state for the\",\n \"with the highest chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None,\",\n \"np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\"\",\n \"feature y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y)\",\n \"of shape [# input features] An element is True iff its corresponding feature\",\n \"the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters =\",\n \"fitted by verifying the presence of fitted attributes (ending with a trailing #\",\n \"between each two features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df =\",\n \"clustering threshold for the Hierarchial clustering random_state - random state for the RandomForestClassifier.\",\n \"BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest,\",\n \"mask indicating which features are selected Returns ---------- mask - boolean array of\",\n \"represents the number of features selected (=number of clusters) The attribute self.selected_features_ is\",\n \"selected for retention. \\\"\\\"\\\" # Checks if the estimator is fitted by verifying\",\n \"each two features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X)\",\n \"of features selected (=number of clusters) The attribute self.selected_features_ is a list of\",\n \"x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x - feature\",\n \"self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals\",\n \"np import sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster\",\n \"self.n_features_ represents the number of features selected (=number of clusters) The attribute self.selected_features_\",\n \"attribute self.selected_features_ is a list of indexes that correspond to the selected features\",\n \"chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer\",\n \"is fitted by verifying the presence of fitted attributes (ending with a trailing\",\n \"sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from\",\n \"hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier\",\n \"as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import\",\n \"---------- a list of lists, each list represents a cluster and contains the\",\n \"clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted\",\n \"= ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return\",\n \"retention. \\\"\\\"\\\" # Checks if the estimator is fitted by verifying the presence\",\n \"mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k\",\n \"def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x,\",\n \"/ n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each\",\n \"indexes of features belonging to the cluster \\\"\\\"\\\" # array of len(X) -\",\n \"= self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals)\",\n \"the SelectKBest features function threshold - clustering threshold for the Hierarchial clustering random_state\",\n \"is the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold,\",\n \"\\\"\\\"\\\" Calculate distance matrix between each two features in X, each value is\",\n \"- feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)),\",\n \"each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix =\",\n \"import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from\",\n \"_phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x -\",\n \"An element is True iff its corresponding feature is selected for retention. \\\"\\\"\\\"\",\n \"x column y - feature y column Returns ---------- phi coefficient value \\\"\\\"\\\"\",\n \"is selected for retention. \\\"\\\"\\\" # Checks if the estimator is fitted by\",\n \"mask - boolean array of shape [# input features] An element is True\",\n \"column y - feature y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix\",\n \"with the highest chi2 score versus y. The attribute self.n_features_ represents the number\",\n \"highest chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix\",\n \"in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef)\",\n \"threshold for the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult\",\n \"hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns\",\n \"- X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage =\",\n \"selection pipeline Parameters ---------- k - the k parameter for the SelectKBest features\",\n \"features in X according to dist_matrix, and selects a feature from each cluster\",\n \"chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self,\",\n \"import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import\",\n \"otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ),\",\n \"SelectKBest features function threshold - clustering threshold for the Hierarchial clustering random_state -\",\n \"feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage):\",\n \"distance matrix between each two features in X, each value is 1-phi_correlation \\\"\\\"\\\"\",\n \"and selects a feature from each cluster with the highest chi2 score of\",\n \"= clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask\",\n \"fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and self.threshold,\",\n \"to the cluster \\\"\\\"\\\" # array of len(X) - array[i] is the cluster\",\n \"- feature y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x,\",\n \"attributes (ending with a trailing # underscore) and otherwise raises a NotFittedError with\",\n \"the estimator is fitted by verifying the presence of fitted attributes (ending with\",\n \"if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ =\",\n \"y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def\",\n \"sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ =\",\n \"is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 -\",\n \"cluster \\\"\\\"\\\" # array of len(X) - array[i] is the cluster number to\",\n \"the number of features selected (=number of clusters) The attribute self.selected_features_ is a\",\n \"a cluster and contains the indexes of features belonging to the cluster \\\"\\\"\\\"\",\n \"SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in X according to\",\n \"Parameters ---------- x - feature x column y - feature y column Returns\",\n \"clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ----------\",\n \"BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in X according to dist_matrix,\",\n \"cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def\",\n \"return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features are\",\n \"True iff its corresponding feature is selected for retention. \\\"\\\"\\\" # Checks if\",\n \"selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)),\",\n \"self.threshold, and selects a feature from each cluster with the highest chi2 score\",\n \"mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold,\",\n \"phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n =\",\n \"for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self):\",\n \"cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X\",\n \"for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline\",\n \"import numpy as np import sklearn import pandas as pd import scipy.spatial.distance as\",\n \"features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat =\",\n \"\\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix,\",\n \"SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that\",\n \"cluster with the highest chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self,\",\n \"# array of len(X) - array[i] is the cluster number to which sample\",\n \"indicating which features are selected Returns ---------- mask - boolean array of shape\",\n \"_get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features are selected Returns ----------\",\n \"import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import\",\n \"and contains the indexes of features belonging to the cluster \\\"\\\"\\\" # array\",\n \"coefficient between features Parameters ---------- x - feature x column y - feature\",\n \"dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature\",\n \"import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from\",\n \"pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2,\",\n \"ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage\",\n \"array of len(X) - array[i] is the cluster number to which sample i\",\n \"enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features\",\n \"return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ----------\",\n \"y - feature y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix =\",\n \"created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents\",\n \"cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx\",\n \"underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask =\",\n \"correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage()\",\n \"---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n\",\n \"\\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self,\",\n \"= hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id,\",\n \"method=method) Returns ---------- a list of lists, each list represents a cluster and\",\n \"chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ =\",\n \"- the k parameter for the SelectKBest features function threshold - clustering threshold\",\n \"X, y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and\",\n \"to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {}\",\n \"Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects a feature\",\n \"sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class\",\n \"boolean array of shape [# input features] An element is True iff its\",\n \"chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix =\",\n \"sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy\",\n \"_corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform\",\n \"verifying the presence of fitted attributes (ending with a trailing # underscore) and\",\n \"coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum()\",\n \"the features in X according to dist_matrix, and selects a feature from each\",\n \"value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr\",\n \"is True iff its corresponding feature is selected for retention. \\\"\\\"\\\" # Checks\",\n \"self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) #\",\n \"chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for\",\n \"cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids):\",\n \"Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns\",\n \"y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return\",\n \"Calculate distance matrix between each two features in X, each value is 1-phi_correlation\",\n \"sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection\",\n \"using self.dist_matrix and self.threshold, and selects a feature from each cluster with the\",\n \"features belonging to the cluster \\\"\\\"\\\" # array of len(X) - array[i] is\",\n \"value: 0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize',\",\n \"selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features\",\n \"= X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f},\",\n \"method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage\",\n \"return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns) using\",\n \"threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates\",\n \"from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline\",\n \"= sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_\",\n \"linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals =\",\n \"\\\"\\\"\\\" A transformer that clusters the features in X according to dist_matrix, and\",\n \"= confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\"\",\n \"X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix =\",\n \"random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature\",\n \"and self.threshold, and selects a feature from each cluster with the highest chi2\",\n \"= Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return pipeline\",\n \"dist_matrix, and selects a feature from each cluster with the highest chi2 score\",\n \"its corresponding feature is selected for retention. \\\"\\\"\\\" # Checks if the estimator\",\n \"from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\"\",\n \"from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from\",\n \"self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi\",\n \"function threshold - clustering threshold for the Hierarchial clustering random_state - random state\",\n \"selects a feature from each cluster with the highest chi2 score of X[feature]\",\n \"Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),\",\n \"selected (=number of clusters) The attribute self.selected_features_ is a list of indexes that\",\n \"import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import\",\n \"Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in X\",\n \"self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return\",\n \"shape [# input features] An element is True iff its corresponding feature is\",\n \"def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram\",\n \"[chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def\",\n \"scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base\",\n \"list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix\",\n \"transformer that clusters the features in X according to dist_matrix, and selects a\",\n \"cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters\",\n \"number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx =\",\n \"[]).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns)\",\n \"of clusters) The attribute self.selected_features_ is a list of indexes that correspond to\",\n \"= {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self,\",\n \"given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask\",\n \"def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two features in X,\",\n \"that clusters the features in X according to dist_matrix, and selects a feature\",\n \"\\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects a\",\n \"and selects a feature from each cluster with the highest chi2 score versus\",\n \"list of indexes that correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix:\",\n \"array of shape [# input features] An element is True iff its corresponding\",\n \"\\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k - the k parameter for\",\n \"Creates feature selection pipeline Parameters ---------- k - the k parameter for the\",\n \"feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self,\",\n \"def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x\",\n \"\\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __\",\n \"sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A\",\n \"The attribute self.selected_features_ is a list of indexes that correspond to the selected\",\n \"as np import sklearn import pandas as pd import scipy.spatial.distance as ssd from\",\n \"the cluster \\\"\\\"\\\" # array of len(X) - array[i] is the cluster number\",\n \"according to dist_matrix, and selects a feature from each cluster with the highest\",\n \"NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] =\",\n \"trailing # underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self)\",\n \"for the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value:\",\n \"(ending with a trailing # underscore) and otherwise raises a NotFittedError with the\",\n \"the boolean mask indicating which features are selected Returns ---------- mask - boolean\",\n \"list of lists, each list represents a cluster and contains the indexes of\",\n \"random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k - the k parameter\",\n \"a feature from each cluster with the highest chi2 score versus y. The\",\n \"features are selected Returns ---------- mask - boolean array of shape [# input\",\n \"a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_]\",\n \"of indexes that correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X)\",\n \"feature selection pipeline Parameters ---------- k - the k parameter for the SelectKBest\",\n \"a list of lists, each list represents a cluster and contains the indexes\",\n \"__init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y):\",\n \"---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list\",\n \"X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def\",\n \"dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\"\",\n \"iff its corresponding feature is selected for retention. \\\"\\\"\\\" # Checks if the\",\n \"is a list of indexes that correspond to the selected features \\\"\\\"\\\" if\",\n \"the highest chi2 score versus y. The attribute self.n_features_ represents the number of\",\n \"cluster and contains the indexes of features belonging to the cluster \\\"\\\"\\\" #\",\n \"---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best',\",\n \"import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in\",\n \"selects a feature from each cluster with the highest chi2 score versus y.\",\n \"Checks if the estimator is fitted by verifying the presence of fitted attributes\",\n \"\\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))])\",\n \"SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters\",\n \"that correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage =\",\n \"parameter for the SelectKBest features function threshold - clustering threshold for the Hierarchial\",\n \"the indexes of features belonging to the cluster \\\"\\\"\\\" # array of len(X)\",\n \"each list represents a cluster and contains the indexes of features belonging to\",\n \"= hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters\",\n \"threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k - the k\",\n \"are selected Returns ---------- mask - boolean array of shape [# input features]\",\n \"\\\"\\\"\\\" # Checks if the estimator is fitted by verifying the presence of\",\n \"- linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists,\",\n \"by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents a\",\n \"and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_,\",\n \"estimator is fitted by verifying the presence of fitted attributes (ending with a\",\n \"fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_\",\n \"[# input features] An element is True iff its corresponding feature is selected\",\n \"k parameter for the SelectKBest features function threshold - clustering threshold for the\",\n \"the highest chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1):\",\n \"import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer\",\n \"each cluster with the highest chi2 score versus y. The attribute self.n_features_ represents\",\n \"hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ----------\",\n \"chi2 score versus y. The attribute self.n_features_ represents the number of features selected\",\n \"idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\"\",\n \"the k parameter for the SelectKBest features function threshold - clustering threshold for\",\n \"sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx,\",\n \"= X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed\",\n \"class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in X according\",\n \"a trailing # underscore) and otherwise raises a NotFittedError with the given message.\",\n \"from each cluster with the highest chi2 score versus y. The attribute self.n_features_\",\n \"cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\"\",\n \"of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold\",\n \"# underscore) and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask\",\n \"feature from each cluster with the highest chi2 score of X[feature] versus y\",\n \"self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters\",\n \"features function threshold - clustering threshold for the Hierarchial clustering random_state - random\",\n \"from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from\",\n \"(X columns) using self.dist_matrix and self.threshold, and selects a feature from each cluster\",\n \"from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from\",\n \"criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values())\",\n \"def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and\",\n \"CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator):\",\n \"= 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline\",\n \"for retention. \\\"\\\"\\\" # Checks if the estimator is fitted by verifying the\",\n \"in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the\",\n \"pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf',\",\n \"Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method)\",\n \"self.dist_matrix and self.threshold, and selects a feature from each cluster with the highest\",\n \"pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in\",\n \"boolean mask indicating which features are selected Returns ---------- mask - boolean array\",\n \"y. The attribute self.n_features_ represents the number of features selected (=number of clusters)\",\n \"ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator\",\n \"a list of indexes that correspond to the selected features \\\"\\\"\\\" if not\",\n \"random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline\",\n \"dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list\",\n \"of lists, each list represents a cluster and contains the indexes of features\",\n \"(=number of clusters) The attribute self.selected_features_ is a list of indexes that correspond\",\n \"= np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix\",\n \"the features (X columns) using self.dist_matrix and self.threshold, and selects a feature from\",\n \"hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list represents a cluster\",\n \"import sklearn import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import\",\n \"y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and self.threshold, and selects\",\n \"clustering random_state - random state for the RandomForestClassifier. Deafult value: 0 Returns ----------\",\n \"between features Parameters ---------- x - feature x column y - feature y\",\n \"= threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters\",\n \"n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X):\",\n \"= self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes\",\n \"the presence of fitted attributes (ending with a trailing # underscore) and otherwise\",\n \"from each cluster with the highest chi2 score of X[feature] versus y \\\"\\\"\\\"\",\n \"return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two features\",\n \"feature from each cluster with the highest chi2 score versus y. The attribute\",\n \"from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin\",\n \"the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance')\",\n \"k - the k parameter for the SelectKBest features function threshold - clustering\",\n \"numpy as np import sklearn import pandas as pd import scipy.spatial.distance as ssd\",\n \"number of features selected (=number of clusters) The attribute self.selected_features_ is a list\",\n \"list represents a cluster and contains the indexes of features belonging to the\",\n \"if the estimator is fitted by verifying the presence of fitted attributes (ending\",\n \"presence of fitted attributes (ending with a trailing # underscore) and otherwise raises\",\n \"X): \\\"\\\"\\\" Calculate distance matrix between each two features in X, each value\",\n \"two features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat\",\n \"X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed =\",\n \"features selected (=number of clusters) The attribute self.selected_features_ is a list of indexes\",\n \"x - feature x column y - feature y column Returns ---------- phi\",\n \"of features belonging to the cluster \\\"\\\"\\\" # array of len(X) - array[i]\",\n \"mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection\",\n \"X according to dist_matrix, and selects a feature from each cluster with the\",\n \"def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k -\",\n \"print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which\",\n \"the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\"\",\n \"n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two\",\n \"pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency\",\n \"which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for\",\n \"scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text\",\n \"attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ =\",\n \"---------- mask - boolean array of shape [# input features] An element is\",\n \"linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage\",\n \"not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X,\",\n \"len(X) - array[i] is the cluster number to which sample i belongs cluster_ids\",\n \"for the SelectKBest features function threshold - clustering threshold for the Hierarchial clustering\",\n \"features Parameters ---------- x - feature x column y - feature y column\",\n \"corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance\",\n \"feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster',\",\n \"each cluster with the highest chi2 score of X[feature] versus y \\\"\\\"\\\" def\",\n \"pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats\",\n \"__ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1]\",\n \"matrix between each two features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df\",\n \"- boolean array of shape [# input features] An element is True iff\",\n \"X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix\",\n \"array[i] is the cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage,\",\n \"raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool)\",\n \"in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get\",\n \"score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix\",\n \"= chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def\",\n \"columns) using self.dist_matrix and self.threshold, and selects a feature from each cluster with\",\n \"clusters) The attribute self.selected_features_ is a list of indexes that correspond to the\",\n \"the Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult value: 0\",\n \"represents a cluster and contains the indexes of features belonging to the cluster\",\n \"clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating\",\n \"by verifying the presence of fitted attributes (ending with a trailing # underscore)\",\n \"scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble\",\n \"1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage\",\n \"= [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self\",\n \"score versus y. The attribute self.n_features_ represents the number of features selected (=number\",\n \"attribute self.n_features_ represents the number of features selected (=number of clusters) The attribute\",\n \"self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y)\",\n \"y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2\",\n \"_calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two features in X, each\",\n \"sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k,\",\n \"def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\"\",\n \"features (X columns) using self.dist_matrix and self.threshold, and selects a feature from each\",\n \"RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import\",\n \"self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean\",\n \"with a trailing # underscore) and otherwise raises a NotFittedError with the given\",\n \"self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return\",\n \"features] An element is True iff its corresponding feature is selected for retention.\",\n \"get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k - the\",\n \"linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created by\",\n \"linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each\",\n \"from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the\",\n \"pipeline Parameters ---------- k - the k parameter for the SelectKBest features function\",\n \"= pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster\",\n \"of len(X) - array[i] is the cluster number to which sample i belongs\",\n \"confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2\",\n \"method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical\",\n \"threshold - clustering threshold for the Hierarchial clustering random_state - random state for\",\n \"0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False,\",\n \"linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering\",\n \"Calculates phi coefficient between features Parameters ---------- x - feature x column y\",\n \"import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin,\",\n \"pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return\",\n \"\\\"\\\"\\\" # array of len(X) - array[i] is the cluster number to which\",\n \"phi coefficient between features Parameters ---------- x - feature x column y -\",\n \"selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage)\",\n \"in X according to dist_matrix, and selects a feature from each cluster with\",\n \"Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a\",\n \"threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ----------\",\n \"\\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed\",\n \"= dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient\",\n \"as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import\",\n \"= 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'):\",\n \"dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between\",\n \"Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline =\",\n \"\\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x - feature x column\",\n \"self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def\",\n \"np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between\",\n \"self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_\",\n \"---------- x - feature x column y - feature y column Returns ----------\",\n \"confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate\",\n \"corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two features in\",\n \"chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr\",\n \"= pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 /\",\n \"X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}')\",\n \"self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features are selected\",\n \"return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage -\",\n \"sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features\",\n \"versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold\",\n \"belonging to the cluster \\\"\\\"\\\" # array of len(X) - array[i] is the\",\n \"{} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X,\",\n \"Returns ---------- mask - boolean array of shape [# input features] An element\",\n \"which features are selected Returns ---------- mask - boolean array of shape [#\",\n \"- random state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline -\",\n \"indexes that correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage\",\n \"pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n)\",\n \"belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in\",\n \"column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 =\",\n \"to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters\",\n \"RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline\",\n \"element is True iff its corresponding feature is selected for retention. \\\"\\\"\\\" #\",\n \"\\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr =\",\n \"selected Returns ---------- mask - boolean array of shape [# input features] An\",\n \"- array[i] is the cluster number to which sample i belongs cluster_ids =\",\n \"pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix\",\n \"state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection\",\n \"the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return\",\n \"# fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters]\",\n \"input features] An element is True iff its corresponding feature is selected for\",\n \"self.selected_features_ is a list of indexes that correspond to the selected features \\\"\\\"\\\"\",\n \"= pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix)\",\n \"= feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self,\",\n \"self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features\",\n \"X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix,\",\n \"corresponding feature is selected for retention. \\\"\\\"\\\" # Checks if the estimator is\",\n \"of fitted attributes (ending with a trailing # underscore) and otherwise raises a\",\n \"import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline\",\n \"A transformer that clusters the features in X according to dist_matrix, and selects\",\n \"for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y):\",\n \"Returns ---------- a list of lists, each list represents a cluster and contains\",\n \"message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def\",\n \"# Checks if the estimator is fitted by verifying the presence of fitted\",\n \"The attribute self.n_features_ represents the number of features selected (=number of clusters) The\",\n \"clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the\",\n \"hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx)\",\n \"y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax()\",\n \"a feature from each cluster with the highest chi2 score of X[feature] versus\",\n \"), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates\",\n \"versus y. The attribute self.n_features_ represents the number of features selected (=number of\",\n \"1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters\",\n \"= np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0):\",\n \"feature is selected for retention. \\\"\\\"\\\" # Checks if the estimator is fitted\",\n \"Parameters ---------- k - the k parameter for the SelectKBest features function threshold\",\n \"lists, each list represents a cluster and contains the indexes of features belonging\",\n \"cluster with the highest chi2 score versus y. The attribute self.n_features_ represents the\",\n \"y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x - feature x\",\n \"linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of\",\n \"feature x column y - feature y column Returns ---------- phi coefficient value\",\n \"Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0]\",\n \"fitted attributes (ending with a trailing # underscore) and otherwise raises a NotFittedError\",\n \"i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id\"\n]","isConversational":false}}},{"rowIdx":3482,"cells":{"ngram":{"kind":"list like","value":["\"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\",","= webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input","time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md')","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data =","browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search time.sleep(2)","maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input","program') break if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice), URL)","3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md')","= WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search =","safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input ==","\"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() #","= BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver","Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window()","actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0): content =","button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel","URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search","element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but will work","WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0]","= requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input","if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row","BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions","from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate","elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>')","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window()","while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice) except ValueError:","search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() #","convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def","item.text print(text) browser.back() print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for","= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\n' *","visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari","raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much","search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\",","import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from","dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\n'","requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC","mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0): content","element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = '","'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\",","unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\",","search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data =","TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\",","from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import","selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains","but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8):","try: choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice not","for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0): content = requests.get(URL)","3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google","browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text)","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content","from bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from","EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver","the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input","TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for","thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and","webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed","== 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)","## ActionChains are not supported in safari but will work on other browser","import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\",","tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds","# Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over","# For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return","except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input,","= [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search","webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel","= webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility","print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver = webdriver.Safari()","= webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google","BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver =","search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5):","Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search =","time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException","\"Demonstrate navigation\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup =","Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search","time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in","for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\"))","search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and extract additional","webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')","visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection():","from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES =","selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time","\"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search","selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions","for the button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))","search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input","time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for","3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')","elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility","EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate","much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser =","row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search =","search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL)","3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading","visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')","= input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop","from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import","3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for","printSelection() choice = input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid","search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google","== 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility","returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =","len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection() choice","5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed():","result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for","3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/')","# Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser =","on thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required","webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input ==","search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle","3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2)","content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting","search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:","google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on","import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import","button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input ==","7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) #","* 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if","= webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel","elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility","in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True):","# Google Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5)","search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility","input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop program')","import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from","try: # proceed if element is found within 3 seconds otherwise raise TimeoutException","time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text","browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser","with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2)","re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions","from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL","') try: choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice","item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the","3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item","search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL)","range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection()","content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input","with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\")","search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button","not supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform()","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList","'__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice) except","elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility","browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds otherwise raise","print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements:","button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains","import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as","# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser =","6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #","search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but","with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in","time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required","found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except","text = item.text print(text) browser.back() print('\\n' * 5) # For convenient visual def","on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download","== 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search","otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too","dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input ==","selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate","seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took","item in dataList: text = item.text print(text) browser.back() print('\\n' * 5) # For","element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search","driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input == 2):","within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException:","selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys","TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\")","stop program') break if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice),","data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input == 6): browser","took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4):","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari()","except TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3')","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() #","time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5)","as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import","formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") #","element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep","dataList: text = item.text print(text) browser.back() print('\\n' * 5) # For convenient visual","webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search","time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content","== 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL)","TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate","= browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text","elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google","import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from","search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input == 2): browser","= item.text print(text) browser.back() print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements):","== 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search =","sep = ' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter","= browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) #","== '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice)","## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required","item in dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari()","WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by","print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection() choice =","element is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID,","\"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL):","visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window()","elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag","browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with","if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice","time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the","import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected","# with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER)","print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input ==","input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3","Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) search =","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')","data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text =","__name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice =","TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys","the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within","if element is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser,","bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support","search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click()","navigation\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser')","work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser =","and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate","returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but will work on other","the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ##","text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required","\"Demonstrate search on thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate maximizing","Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required","HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\")","ActionChains are not supported in safari but will work on other browser ##","time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in","= 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on","= returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but will work on","browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google","1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\"","search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN)","browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:","# Print row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\")","visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds otherwise","too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser","= driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input == 2): browser =","time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser","listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)):","browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if","expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium","browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input ==","search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3):","ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES","run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) #","with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome()","def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ')","if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i],","selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google","= browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input","4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))","in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input","data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input,","webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search","import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import","def run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify())","ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop","from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from","https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for","choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop program') break","choice = input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input,","screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input ==","selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException","on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and extract additional data\",","Google Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search","browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \"Google Pixel 3\"","= int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)):","Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over =","browser.back() print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in","8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #","visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7):","import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from","URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print","'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel","Print row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search","driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit()","# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported","soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1):","def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:')","selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL =","supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform()","for item in dataList: text = item.text print(text) browser.back() print('\\n' * 5) #","will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser","return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep =","from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import","print(text) browser.back() print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element","5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #","in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0,","int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid","print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button","') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try:","TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input","thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse","== 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') #","browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try:","webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions","visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are","search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item","the button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google","in dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window()","[\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on","on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari()","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser =","= ' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice:","with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input","By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains","= item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for","import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com'","<PASSWORD>') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser","import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from","For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element","visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input ==","# Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element","import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import","= returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the","in dataList: text = item.text print(text) browser.back() print('\\n' * 5) # For convenient","for item in dataList: text = item.text print(text) elif(input == 6): browser =","print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__","ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the","= webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: #","dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() #","for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ ==","use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate","browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search","elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search","printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if","visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data","i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__':","selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By","import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from","== 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)","is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254')))","= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input == 6):","time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data","are not supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136","from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import","\"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search","time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but will","\"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0):","for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i","download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text","element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for","WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate","elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility","additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def","' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ')","browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() #","\"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and extract","# proceed if element is found within 3 seconds otherwise raise TimeoutException element","print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop program')","Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is","search result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions","extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"]","browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2)","Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari()","= webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility","== 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with","other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window()","element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in","browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with","search on thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\",","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =","Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup","content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back()","returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for","\"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1]","Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions","requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input ==","== 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)","browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not","from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import","time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari()","driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari()","data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\n' * 5)","for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found","# Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) search","visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data","choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in","proceed if element is found within 3 seconds otherwise raise TimeoutException element =","0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML","2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google","input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop program') break","browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with"],"string":"[\n \"\\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\",\",\n \"= webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md')\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data =\",\n \"browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search time.sleep(2)\",\n \"maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input\",\n \"program') break if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice), URL)\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md')\",\n \"= WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search =\",\n \"safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input ==\",\n \"\\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() #\",\n \"= BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver\",\n \"Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window()\",\n \"actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content =\",\n \"button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel\",\n \"URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search\",\n \"element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but will work\",\n \"WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0]\",\n \"= requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input\",\n \"if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row\",\n \"BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions\",\n \"from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate\",\n \"elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>')\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window()\",\n \"while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice) except ValueError:\",\n \"search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() #\",\n \"convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def\",\n \"item.text print(text) browser.back() print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for\",\n \"= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\\\n' *\",\n \"visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari\",\n \"raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much\",\n \"search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\",\",\n \"import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from\",\n \"dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\\\n'\",\n \"requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC\",\n \"mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content\",\n \"element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = '\",\n \"'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\",\",\n \"unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\",\",\n \"search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data =\",\n \"TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\",\",\n \"from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import\",\n \"selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains\",\n \"but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8):\",\n \"try: choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice not\",\n \"for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content = requests.get(URL)\",\n \"3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google\",\n \"browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text)\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content\",\n \"from bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from\",\n \"EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver\",\n \"the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input\",\n \"TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for\",\n \"thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and\",\n \"webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed\",\n \"== 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"## ActionChains are not supported in safari but will work on other browser\",\n \"import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\",\",\n \"tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds\",\n \"# Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over\",\n \"# For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return\",\n \"except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input,\",\n \"= [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search\",\n \"webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel\",\n \"= webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility\",\n \"print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver = webdriver.Safari()\",\n \"= webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google\",\n \"BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1): driver =\",\n \"search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5):\",\n \"Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search =\",\n \"time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException\",\n \"\\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup =\",\n \"Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search\",\n \"time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in\",\n \"for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\"))\",\n \"search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract additional\",\n \"webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')\",\n \"visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection():\",\n \"from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES =\",\n \"selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time\",\n \"\\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search\",\n \"selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions\",\n \"for the button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))\",\n \"search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')\",\n \"elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility\",\n \"EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate\",\n \"much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser =\",\n \"row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search =\",\n \"search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL)\",\n \"3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading\",\n \"visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')\",\n \"= input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop\",\n \"from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import\",\n \"3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"printSelection() choice = input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid\",\n \"search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google\",\n \"== 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility\",\n \"returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =\",\n \"len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection() choice\",\n \"5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed():\",\n \"result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for\",\n \"3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/')\",\n \"# Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser =\",\n \"on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input ==\",\n \"search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle\",\n \"3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2)\",\n \"content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting\",\n \"search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:\",\n \"google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on\",\n \"import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import\",\n \"button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input ==\",\n \"7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"* 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if\",\n \"= webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel\",\n \"elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility\",\n \"in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True):\",\n \"# Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5)\",\n \"search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility\",\n \"input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop program')\",\n \"import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from\",\n \"try: # proceed if element is found within 3 seconds otherwise raise TimeoutException\",\n \"time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text\",\n \"browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser\",\n \"with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2)\",\n \"re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions\",\n \"from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL\",\n \"') try: choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice\",\n \"item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item\",\n \"search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL)\",\n \"range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection()\",\n \"content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input\",\n \"with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\")\",\n \"search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"not supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform()\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList\",\n \"'__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice) except\",\n \"elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility\",\n \"browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds otherwise raise\",\n \"print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements:\",\n \"button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains\",\n \"import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as\",\n \"# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser =\",\n \"6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but\",\n \"with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in\",\n \"time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required\",\n \"found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except\",\n \"text = item.text print(text) browser.back() print('\\\\n' * 5) # For convenient visual def\",\n \"on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download\",\n \"== 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search\",\n \"otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too\",\n \"dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input ==\",\n \"selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate\",\n \"seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took\",\n \"item in dataList: text = item.text print(text) browser.back() print('\\\\n' * 5) # For\",\n \"element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search\",\n \"driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2):\",\n \"within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException:\",\n \"selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys\",\n \"TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\")\",\n \"stop program') break if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice),\",\n \"data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input == 6): browser\",\n \"took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4):\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari()\",\n \"except TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3')\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() #\",\n \"time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5)\",\n \"as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import\",\n \"formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") #\",\n \"element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep\",\n \"dataList: text = item.text print(text) browser.back() print('\\\\n' * 5) # For convenient visual\",\n \"webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search\",\n \"time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content\",\n \"== 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate\",\n \"= browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text\",\n \"elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google\",\n \"import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from\",\n \"search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser\",\n \"= item.text print(text) browser.back() print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements):\",\n \"== 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search =\",\n \"sep = ' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter\",\n \"= browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) #\",\n \"== '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice = int(choice)\",\n \"## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"item in dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari()\",\n \"WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by\",\n \"print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True): printSelection() choice =\",\n \"element is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID,\",\n \"\\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL):\",\n \"visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window()\",\n \"elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the input tag\",\n \"browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with\",\n \"if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice\",\n \"time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected\",\n \"# with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER)\",\n \"print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input ==\",\n \"input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3\",\n \"Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) search =\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')\",\n \"data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text =\",\n \"__name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice =\",\n \"TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys\",\n \"the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within\",\n \"if element is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser,\",\n \"bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support\",\n \"search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click()\",\n \"navigation\\\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser')\",\n \"work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser =\",\n \"and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate\",\n \"returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but will work on other\",\n \"the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ##\",\n \"text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"\\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing\",\n \"Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\")\",\n \"ActionChains are not supported in safari but will work on other browser ##\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in\",\n \"= 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on\",\n \"= returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but will work on\",\n \"browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google\",\n \"1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\"\",\n \"search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN)\",\n \"browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:\",\n \"# Print row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\")\",\n \"visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3 seconds otherwise\",\n \"too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser\",\n \"= driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser =\",\n \"time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser\",\n \"listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)):\",\n \"browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if\",\n \"expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium\",\n \"browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input ==\",\n \"search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3):\",\n \"ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES\",\n \"run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) #\",\n \"with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome()\",\n \"def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ')\",\n \"if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i],\",\n \"selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google\",\n \"= browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input\",\n \"4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))\",\n \"in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input\",\n \"data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input,\",\n \"webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility search\",\n \"import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import\",\n \"def run(input, URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify())\",\n \"ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop\",\n \"from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from\",\n \"https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"choice: ') try: choice = int(choice) except ValueError: print('Invalid input, stop program') break\",\n \"choice = input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid input,\",\n \"screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input ==\",\n \"selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException\",\n \"on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\",\",\n \"Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search\",\n \"browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search \\\"Google Pixel 3\\\"\",\n \"= int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)):\",\n \"Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over =\",\n \"browser.back() print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in\",\n \"8): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7):\",\n \"import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from\",\n \"URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print\",\n \"'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel\",\n \"Print row with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search\",\n \"driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit()\",\n \"# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported\",\n \"soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input == 1):\",\n \"def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:')\",\n \"selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL =\",\n \"supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform()\",\n \"for item in dataList: text = item.text print(text) browser.back() print('\\\\n' * 5) #\",\n \"will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser\",\n \"return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep =\",\n \"from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import Keys import\",\n \"print(text) browser.back() print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element\",\n \"5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0,\",\n \"int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid\",\n \"print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ') try:\",\n \"TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input\",\n \"thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse\",\n \"== 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') #\",\n \"browser = webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try:\",\n \"webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions\",\n \"visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are\",\n \"search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item\",\n \"the button visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google\",\n \"in dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window()\",\n \"[\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on\",\n \"on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari()\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser =\",\n \"= ' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice:\",\n \"with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input\",\n \"By from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains\",\n \"= item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com'\",\n \"<PASSWORD>') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser\",\n \"import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from\",\n \"For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element\",\n \"visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input ==\",\n \"# Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element\",\n \"import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import\",\n \"= returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the\",\n \"in dataList: text = item.text print(text) browser.back() print('\\\\n' * 5) # For convenient\",\n \"for item in dataList: text = item.text print(text) elif(input == 6): browser =\",\n \"print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__\",\n \"ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"= webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: #\",\n \"dataList: text = item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() #\",\n \"for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ ==\",\n \"use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate\",\n \"browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google Search\",\n \"elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search\",\n \"printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if\",\n \"visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data\",\n \"i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__':\",\n \"selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By\",\n \"import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from\",\n \"== 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"is found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254')))\",\n \"= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input == 6):\",\n \"time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data\",\n \"are not supported in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136\",\n \"from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import\",\n \"\\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search\",\n \"time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but will\",\n \"\\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0):\",\n \"for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i\",\n \"download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text\",\n \"element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for\",\n \"WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate\",\n \"elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility\",\n \"additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def\",\n \"' ') if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ')\",\n \"browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() #\",\n \"\\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract\",\n \"# proceed if element is found within 3 seconds otherwise raise TimeoutException element\",\n \"print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop program')\",\n \"Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is\",\n \"search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions\",\n \"extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"]\",\n \"browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2)\",\n \"Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari()\",\n \"= webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) # with visibility\",\n \"== 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with\",\n \"other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window()\",\n \"element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i in\",\n \"browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) # with\",\n \"search on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\",\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =\",\n \"Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content = requests.get(URL) soup\",\n \"content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back()\",\n \"returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for\",\n \"\\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1]\",\n \"Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions\",\n \"requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input ==\",\n \"== 5): browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not\",\n \"from selenium import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import\",\n \"time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari()\",\n \"driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel<PASSWORD>\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari()\",\n \"data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back() print('\\\\n' * 5)\",\n \"for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found\",\n \"# Required for the button visibility browser.get(URL) # with visibility search time.sleep(2) search\",\n \"visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data\",\n \"choice = int(choice) except ValueError: print('Invalid input, stop program') break if(choice not in\",\n \"proceed if element is found within 3 seconds otherwise raise TimeoutException element =\",\n \"0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML\",\n \"2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google <PASSWORD>') # Google\",\n \"input, stop program') break if(choice not in range(0,9)): print('Invalid input, stop program') break\",\n \"browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with\"\n]","isConversational":false}}},{"rowIdx":3483,"cells":{"ngram":{"kind":"list like","value":["system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the","the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic","handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP server\") createServer() #","directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \"\": # read","# send the OK response back to the TECO retString = \"OK\\n\"; self.send(retString.encode())","# last line was cutt off and the rest should come with the","next read self.tcpLineBuffer = lastLine for line in lines: # remove a terminating","next set of byte from the data socket data = self.recv(LINEBUFFERLEN) if data","\"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the","LOG(\"*** receiveCallback ***\") # read the next set of byte from the data","from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has to","(see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has","None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\")","\"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK response back","The Space Python Library is free software; you can redistribute it and/or *","initialise the system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler()","receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback ***\")","exit has been entered (case insensitive) upperLine = line.upper() if (upperLine == \"X\")","requested\") # send the OK response back to the client retString = \"OK\\n\"","= \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the","line = line[:-1] # terminate the client connection if exit has been entered","# * # The Space Python Library is distributed in the hope that","client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \"","Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR","send the OK response back to the client retString = \"OK\\n\" self.send(retString.encode()) #","of the MIT License as published by the * # Massachusetts Institute of","in a special way and can not be # processed directly lastLine =","the * # Massachusetts Institute of Technology. * # * # The Space","LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract the lines from the","LOG(\"line = \" + line) return 0 ############# # functions # ############# #","the data socket data = self.recv(LINEBUFFERLEN) if data == None: # client is","line): \"\"\"Callback when a client has send a data line\"\"\" LOG(\"line = \"","entered (case insensitive) upperLine = line.upper() if (upperLine == \"X\") or (upperLine ==","\"QUIT\"): LOG(\"Quit requested\") # send the OK response back to the client retString","\"EXIT\"): LOG(\"Exit requested\") # send the OK response back to the client retString","not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction():","= 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of","under the terms of the MIT License as published by the * #","+ line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration():","initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask =","= \" + line) return 0 ############# # functions # ############# # -----------------------------------------------------------------------------","<NAME>, Austria * # * # The Space Python Library is free software;","lines = tcpLineBuffer.split(\"\\n\") # the last line has to be handled in a","OK response back to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the","idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main","# terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus =","create the TCP server LOG(\"Open the TCP server\") createServer() # start the tasks","with the next read self.tcpLineBuffer = lastLine for line in lines: # remove","= tcpLineBuffer.split(\"\\n\") # the last line has to be handled in a special","# functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([","and the rest should come with the next read self.tcpLineBuffer = lastLine for","is free software; you can redistribute it and/or * # modify it under","= lastLine for line in lines: # remove a terminating \"\\r\" for clients","\"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server =","* #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import","UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# #","= \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback","def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer","= self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the OK response back","functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\",","has send a data line\"\"\" LOG(\"line = \" + line) return 0 #############","############# LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer):","free software; you can redistribute it and/or * # modify it under under","complete (incl. \"\\n\") pass else: # last line was cutt off and the","will be useful, * # but WITHOUT ANY WARRANTY; without even the implied","server LOG(\"Open the TCP server\") createServer() # start the tasks LOG(\"start modelTask...\") modelTask.start()","pstatus == 0: LOG(\"OK\") # send the OK response back to the TECO","############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\"","\"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK response back","== 0: LOG(\"OK\") # send the OK response back to the TECO retString","data line\"\"\" LOG(\"line = \" + line) return 0 ############# # functions #","or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK response back to","the TCP server LOG(\"Open the TCP server\") createServer() # start the tasks LOG(\"start","UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open","0: LOG(\"OK\") # send the OK response back to the TECO retString =","0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system","def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1)","the implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.","\"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" #","the MIT License as published by the * # Massachusetts Institute of Technology.","pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the OK response","LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString = \"Error:","\"__main__\": # initialise the system configuration initConfiguration() # initialise the console handler consoleHandler","= self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input:","more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS","response back to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client","socket data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected","* # The Space Python Library is free software; you can redistribute it","(C) 2018, <NAME>, Austria * # * # The Space Python Library is","line has to be handled in a special way and can not be","line[-1] == \"\\r\": line = line[:-1] # terminate the client connection if exit","read the next set of byte from the data socket data = self.recv(LINEBUFFERLEN)","input: extract the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the","way and can not be # processed directly lastLine = lines[-1] lines =","self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString","FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * # for more","############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes # ###########","# * # The Space Python Library is free software; you can redistribute","= \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has","self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") #","= UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server","was complete (incl. \"\\n\") pass else: # last line was cutt off and","from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP #############","PARTICULAR PURPOSE. See the MIT License * # for more details. * #******************************************************************************","import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants","if data == None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer","failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client","the system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() #","UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes","automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer)","register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP","\" + line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def","LOG(\"--- idle ---\") ######## # main # ######## if __name__ == \"__main__\": #","# read the next set of byte from the data socket data =","\"\\r\" for clients like telnet if line[-1] == \"\\r\": line = line[:-1] #","\"\": # read of the data was complete (incl. \"\\n\") pass else: #","# create the TCP server LOG(\"Open the TCP server\") createServer() # start the","\"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate","UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask):","WARRANTY; without even the implied warranty of * # MERCHANTABILITY or FITNESS FOR","the next read self.tcpLineBuffer = lastLine for line in lines: # remove a","+= data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract the lines","# the last line has to be handled in a special way and","(case insensitive) upperLine = line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"):","# The Space Python Library is distributed in the hope that it will","the client connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine == \"QUIT\"):","retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to","data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract the lines from","----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main # ########","Error response back to the client: retString = \"Error: execution failed (see log)!\\n\"","cutt off and the rest should come with the next read self.tcpLineBuffer =","when a client has send a data line\"\"\" LOG(\"line = \" + line)","256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\"","remove a terminating \"\\r\" for clients like telnet if line[-1] == \"\\r\": line","connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus ==","read self.tcpLineBuffer = lastLine for line in lines: # remove a terminating \"\\r\"","system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise","even the implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR","client connection if exit has been entered (case insensitive) upperLine = line.upper() if","***\") # read the next set of byte from the data socket data","def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback","processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \"\": #","ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY or FITNESS","clients like telnet if line[-1] == \"\\r\": line = line[:-1] # terminate the","details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import","self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract","be handled in a special way and can not be # processed directly","self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine == \"Q\") or","PURPOSE. See the MIT License * # for more details. * #****************************************************************************** #","processLine(self, line): \"\"\"Callback when a client has send a data line\"\"\" LOG(\"line =","UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler","the MIT License * # for more details. * #****************************************************************************** # Unit Tests","= lines[:-1] if lastLine == \"\": # read of the data was complete","# remove a terminating \"\\r\" for clients like telnet if line[-1] == \"\\r\":","line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise","the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if","= self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return tcpLineBuffer","# ######## if __name__ == \"__main__\": # initialise the system configuration initConfiguration() #","classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def","the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create","a data line\"\"\" LOG(\"line = \" + line) return 0 ############# # functions","can not be # processed directly lastLine = lines[-1] lines = lines[:-1] if","retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line):","= line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") #","self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return tcpLineBuffer =","the rest should come with the next read self.tcpLineBuffer = lastLine for line","without even the implied warranty of * # MERCHANTABILITY or FITNESS FOR A","(incl. \"\\n\") pass else: # last line was cutt off and the rest","server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function","of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask","useful, * # but WITHOUT ANY WARRANTY; without even the implied warranty of","client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if","be # processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine ==","data was complete (incl. \"\\n\") pass else: # last line was cutt off","LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # #############","input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the OK","* # Massachusetts Institute of Technology. * # * # The Space Python","receiveCallback ***\") # read the next set of byte from the data socket","# client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer:","# initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask","console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) #","# --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has send a data","for clients like telnet if line[-1] == \"\\r\": line = line[:-1] # terminate","Institute of Technology. * # * # The Space Python Library is distributed","Massachusetts Institute of Technology. * # * # The Space Python Library is","if line[-1] == \"\\r\": line = line[:-1] # terminate the client connection if","implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See","Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants #","terminating \"\\r\" for clients like telnet if line[-1] == \"\\r\": line = line[:-1]","stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read","constants # ############# LINEBUFFERLEN = 256 ########### # classes # ########### # =============================================================================","= TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() #","software; you can redistribute it and/or * # modify it under under the","License as published by the * # Massachusetts Institute of Technology. * #","handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register","self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client","* # * # The Space Python Library is free software; you can","== \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK response","Library is free software; you can redistribute it and/or * # modify it","UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### #","\"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine == \"Q\")","for line in lines: # remove a terminating \"\\r\" for clients like telnet","__init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer =","return if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send","connection if exit has been entered (case insensitive) upperLine = line.upper() if (upperLine","been entered (case insensitive) upperLine = line.upper() if (upperLine == \"X\") or (upperLine","\"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer():","client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) #","# initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler)","response back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set","send a data line\"\"\" LOG(\"line = \" + line) return 0 ############# #","delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") # send","Technology. * # * # The Space Python Library is distributed in the","modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP server\") createServer() # start","back to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection","(upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK response back to the","== \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK response","pass else: # last line was cutt off and the rest should come","you can redistribute it and/or * # modify it under under the terms","--------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has send a data line\"\"\"","* # for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import","data socket data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically","return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle","* # The Space Python Library is distributed in the hope that it","# read of the data was complete (incl. \"\\n\") pass else: # last","terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line);","retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate","TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # -----------------------------------------------------------------------------","the hope that it will be useful, * # but WITHOUT ANY WARRANTY;","line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has to be handled","# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License *","Library is distributed in the hope that it will be useful, * #","\" + tcpLineBuffer) # handle the input: extract the lines from the line","= \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine ==","of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT","handle the input: extract the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\")","= UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket,","self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus","# terminate the client connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine","should come with the next read self.tcpLineBuffer = lastLine for line in lines:","TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back","modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP","########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr):","come with the next read self.tcpLineBuffer = lastLine for line in lines: #","----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) #","the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True)","# terminate the client connection if exit has been entered (case insensitive) upperLine","============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes","* #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error,","the OK response back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus))","special way and can not be # processed directly lastLine = lines[-1] lines","UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback","like telnet if line[-1] == \"\\r\": line = line[:-1] # terminate the client","createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) #","the input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the","+ tcpLineBuffer) # handle the input: extract the lines from the line buffer","initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def","# for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys","back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the","UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main # ######## if __name__ ==","if pstatus == 0: LOG(\"OK\") # send the OK response back to the","# ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise","self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus == 0:","__name__ == \"__main__\": # initialise the system configuration initConfiguration() # initialise the console","telnet if line[-1] == \"\\r\": line = line[:-1] # terminate the client connection","hope that it will be useful, * # but WITHOUT ANY WARRANTY; without","# ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]])","# Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO,","the input: extract the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") #","server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000,","LOG(\"Quit requested\") # send the OK response back to the client retString =","# ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main #","byte from the data socket data = self.recv(LINEBUFFERLEN) if data == None: #","or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * # for","the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0)","socket, stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback ***\") #","portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a","from the data socket data = self.recv(LINEBUFFERLEN) if data == None: # client","data == None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer","server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction()","--------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"***","== \"__main__\": # initialise the system configuration initConfiguration() # initialise the console handler","of Technology. * # * # The Space Python Library is distributed in","was cutt off and the rest should come with the next read self.tcpLineBuffer","WITHOUT ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY or","2018, <NAME>, Austria * # * # The Space Python Library is free","# but WITHOUT ANY WARRANTY; without even the implied warranty of * #","# main # ######## if __name__ == \"__main__\": # initialise the system configuration","\"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when","the client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def","# modify it under under the terms of the MIT License as published","######## # main # ######## if __name__ == \"__main__\": # initialise the system","(upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK","a client has send a data line\"\"\" LOG(\"line = \" + line) return","# --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send data\"\"\"","the OK response back to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate","to the client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # ---------------------------------------------------------------------------","LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN","LOG(\"Exit requested\") # send the OK response back to the client retString =","lastLine == \"\": # read of the data was complete (incl. \"\\n\") pass","# ############# LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class","if lastLine == \"\": # read of the data was complete (incl. \"\\n\")","idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main # ######## if __name__","line\"\"\" LOG(\"line = \" + line) return 0 ############# # functions # #############","\"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input","has been entered (case insensitive) upperLine = line.upper() if (upperLine == \"X\") or","lastLine = lines[-1] lines = lines[:-1] if lastLine == \"\": # read of","# send the OK response back to the client retString = \"OK\\n\" self.send(retString.encode())","# register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the","set of byte from the data socket data = self.recv(LINEBUFFERLEN) if data ==","when a client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next","rest should come with the next read self.tcpLineBuffer = lastLine for line in","#****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS,","the Error response back to the client: retString = \"Error: execution failed (see","class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\"","TCP server LOG(\"Open the TCP server\") createServer() # start the tasks LOG(\"start modelTask...\")","Austria * # * # The Space Python Library is free software; you","tcpLineBuffer.split(\"\\n\") # the last line has to be handled in a special way","# ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not","def processLine(self, line): \"\"\"Callback when a client has send a data line\"\"\" LOG(\"line","LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass","line[:-1] # terminate the client connection if exit has been entered (case insensitive)","def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main # ######## if","\"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send","######## if __name__ == \"__main__\": # initialise the system configuration initConfiguration() # initialise","LOG(\"OK\") # send the OK response back to the TECO retString = \"OK\\n\";","published by the * # Massachusetts Institute of Technology. * # * #","main # ######## if __name__ == \"__main__\": # initialise the system configuration initConfiguration()","to be handled in a special way and can not be # processed","configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the","has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set of byte","* # but WITHOUT ANY WARRANTY; without even the implied warranty of *","last line was cutt off and the rest should come with the next","send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set of byte from","buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has to be handled in","OK response back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) #","(upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK","else: LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString =","response back to the client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode())","lines: # remove a terminating \"\\r\" for clients like telnet if line[-1] ==","(upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK response back to the","lines[:-1] if lastLine == \"\": # read of the data was complete (incl.","= lines[-1] lines = lines[:-1] if lastLine == \"\": # read of the","connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\")","Python Library is distributed in the hope that it will be useful, *","that it will be useful, * # but WITHOUT ANY WARRANTY; without even","############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\",","the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create","idle ---\") ######## # main # ######## if __name__ == \"__main__\": # initialise","activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle","send the OK response back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else:","\"\"\"Callback when a client has send a data line\"\"\" LOG(\"line = \" +","distributed in the hope that it will be useful, * # but WITHOUT","# initialise the system configuration initConfiguration() # initialise the console handler consoleHandler =","back to the client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) #","lastLine for line in lines: # remove a terminating \"\\r\" for clients like","the client connection if exit has been entered (case insensitive) upperLine = line.upper()","MIT License * # for more details. * #****************************************************************************** # Unit Tests *","Space Python Library is free software; you can redistribute it and/or * #","by the * # Massachusetts Institute of Technology. * # * # The","if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the","self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the OK response back to","has to be handled in a special way and can not be #","lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has","is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" +","can redistribute it and/or * # modify it under under the terms of","# handle the input: extract the lines from the line buffer lines =","return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the","but WITHOUT ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY","# activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"---","only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def","line in lines: # remove a terminating \"\\r\" for clients like telnet if","under under the terms of the MIT License as published by the *","zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\")","line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send","is distributed in the hope that it will be useful, * # but","lines = lines[:-1] if lastLine == \"\": # read of the data was","sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP","the last line has to be handled in a special way and can","the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return","it will be useful, * # but WITHOUT ANY WARRANTY; without even the","Python Library is free software; you can redistribute it and/or * # modify","# constants # ############# LINEBUFFERLEN = 256 ########### # classes # ########### #","== \"\": # read of the data was complete (incl. \"\\n\") pass else:","self.tcpLineBuffer = lastLine for line in lines: # remove a terminating \"\\r\" for","= UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console","* # modify it under under the terms of the MIT License as","idleFunction) LOG(\"--- idle ---\") ######## # main # ######## if __name__ == \"__main__\":","tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the","last line has to be handled in a special way and can not","terms of the MIT License as published by the * # Massachusetts Institute","See the MIT License * # for more details. * #****************************************************************************** # Unit","the next set of byte from the data socket data = self.recv(LINEBUFFERLEN) if","line was cutt off and the rest should come with the next read","execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a","warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the","terminate the client connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine ==","be useful, * # but WITHOUT ANY WARRANTY; without even the implied warranty","client connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit","function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## #","#****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG,","A PARTICULAR PURPOSE. See the MIT License * # for more details. *","off and the rest should come with the next read self.tcpLineBuffer = lastLine","a client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set","self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has send a","# The Space Python Library is free software; you can redistribute it and/or","Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING,","initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model","to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error","read of the data was complete (incl. \"\\n\") pass else: # last line","data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set of byte from the","and can not be # processed directly lastLine = lines[-1] lines = lines[:-1]","import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ###########","it and/or * # modify it under under the terms of the MIT","the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line","else: # last line was cutt off and the rest should come with","= \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the","Space Python Library is distributed in the hope that it will be useful,","#****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The Space Python","########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" #","import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK,","--------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr)","TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle","if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the","to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient();","UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes #","idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ########","not be # processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine","in lines: # remove a terminating \"\\r\" for clients like telnet if line[-1]","retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine","sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction)","it under under the terms of the MIT License as published by the","# Massachusetts Institute of Technology. * # * # The Space Python Library","UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self,","a terminating \"\\r\" for clients like telnet if line[-1] == \"\\r\": line =","# set the Error response back to the client: retString = \"Error: execution","# ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self,","tcpLineBuffer) # handle the input: extract the lines from the line buffer lines","def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # -----------------------------------------------------------------------------","FOR A PARTICULAR PURPOSE. See the MIT License * # for more details.","License * # for more details. * #****************************************************************************** # Unit Tests * #******************************************************************************","\"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask =","[\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server","# (C) 2018, <NAME>, Austria * # * # The Space Python Library","attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # ---------------------------------------------------------------------------","LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256","client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set of","client has send a data line\"\"\" LOG(\"line = \" + line) return 0","the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response","console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP server\") createServer()","initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) #","in the hope that it will be useful, * # but WITHOUT ANY","the data was complete (incl. \"\\n\") pass else: # last line was cutt","* # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License","MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * #","a special way and can not be # processed directly lastLine = lines[-1]","data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return","modify it under under the terms of the MIT License as published by","modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when","UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\"","modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self,","== None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer +=","upperLine = line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\")","= line[:-1] # terminate the client connection if exit has been entered (case","if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def","#!/usr/bin/env python3 #****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The","LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN =","# processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \"\":","\"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if","if __name__ == \"__main__\": # initialise the system configuration initConfiguration() # initialise the","* # * # The Space Python Library is distributed in the hope","model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the","handled in a special way and can not be # processed directly lastLine","The Space Python Library is distributed in the hope that it will be","for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from","---\") ######## # main # ######## if __name__ == \"__main__\": # initialise the","extract the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last","python3 #****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The Space","client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self,","disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) #","of the data was complete (incl. \"\\n\") pass else: # last line was","portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\"","\"\\r\": line = line[:-1] # terminate the client connection if exit has been","MIT License as published by the * # Massachusetts Institute of Technology. *","set the Error response back to the client: retString = \"Error: execution failed","redistribute it and/or * # modify it under under the terms of the","\"\\n\") pass else: # last line was cutt off and the rest should","client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus","\"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the client:","lines[-1] lines = lines[:-1] if lastLine == \"\": # read of the data","log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has send","or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK response back to","the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP server\")","of byte from the data socket data = self.recv(LINEBUFFERLEN) if data == None:","consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the","----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST):","if exit has been entered (case insensitive) upperLine = line.upper() if (upperLine ==","insensitive) upperLine = line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit","== \"EXIT\"): LOG(\"Exit requested\") # send the OK response back to the client","== \"QUIT\"): LOG(\"Quit requested\") # send the OK response back to the client","# classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # ---------------------------------------------------------------------------","tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract the","TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask","the terms of the MIT License as published by the * # Massachusetts","the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has to be","and/or * # modify it under under the terms of the MIT License","# ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"],","sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\")","[\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT))","as published by the * # Massachusetts Institute of Technology. * # *","# --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask,","== \"\\r\": line = line[:-1] # terminate the client connection if exit has","configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP","terminate the client connection if exit has been entered (case insensitive) upperLine =","# delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") #"],"string":"[\n \"system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the\",\n \"the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic\",\n \"handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP server\\\") createServer() #\",\n \"directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": # read\",\n \"# send the OK response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode())\",\n \"# last line was cutt off and the rest should come with the\",\n \"next read self.tcpLineBuffer = lastLine for line in lines: # remove a terminating\",\n \"next set of byte from the data socket data = self.recv(LINEBUFFERLEN) if data\",\n \"\\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the\",\n \"LOG(\\\"*** receiveCallback ***\\\") # read the next set of byte from the data\",\n \"from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to\",\n \"(see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has\",\n \"None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\")\",\n \"\\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back\",\n \"The Space Python Library is free software; you can redistribute it and/or *\",\n \"initialise the system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler()\",\n \"receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\")\",\n \"exit has been entered (case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\")\",\n \"requested\\\") # send the OK response back to the client retString = \\\"OK\\\\n\\\"\",\n \"= \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the\",\n \"line = line[:-1] # terminate the client connection if exit has been entered\",\n \"# * # The Space Python Library is distributed in the hope that\",\n \"client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\"\",\n \"Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR\",\n \"send the OK response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) #\",\n \"of the MIT License as published by the * # Massachusetts Institute of\",\n \"in a special way and can not be # processed directly lastLine =\",\n \"the * # Massachusetts Institute of Technology. * # * # The Space\",\n \"LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract the lines from the\",\n \"LOG(\\\"line = \\\" + line) return 0 ############# # functions # ############# #\",\n \"the data socket data = self.recv(LINEBUFFERLEN) if data == None: # client is\",\n \"line): \\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\"\",\n \"entered (case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine ==\",\n \"\\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back to the client retString\",\n \"\\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back to the client retString\",\n \"not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction():\",\n \"= 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of\",\n \"under the terms of the MIT License as published by the * #\",\n \"+ line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration():\",\n \"initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask =\",\n \"= \\\" + line) return 0 ############# # functions # ############# # -----------------------------------------------------------------------------\",\n \"<NAME>, Austria * # * # The Space Python Library is free software;\",\n \"lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled in a\",\n \"OK response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the\",\n \"idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main\",\n \"# terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus =\",\n \"create the TCP server LOG(\\\"Open the TCP server\\\") createServer() # start the tasks\",\n \"with the next read self.tcpLineBuffer = lastLine for line in lines: # remove\",\n \"= tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled in a special\",\n \"# functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([\",\n \"and the rest should come with the next read self.tcpLineBuffer = lastLine for\",\n \"is free software; you can redistribute it and/or * # modify it under\",\n \"= lastLine for line in lines: # remove a terminating \\\"\\\\r\\\" for clients\",\n \"\\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server =\",\n \"* #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import\",\n \"UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# #\",\n \"= \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback\",\n \"def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer\",\n \"= self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the OK response back\",\n \"functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\",\",\n \"has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 #############\",\n \"############# LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer):\",\n \"free software; you can redistribute it and/or * # modify it under under\",\n \"complete (incl. \\\"\\\\n\\\") pass else: # last line was cutt off and the\",\n \"will be useful, * # but WITHOUT ANY WARRANTY; without even the implied\",\n \"server LOG(\\\"Open the TCP server\\\") createServer() # start the tasks LOG(\\\"start modelTask...\\\") modelTask.start()\",\n \"pstatus == 0: LOG(\\\"OK\\\") # send the OK response back to the TECO\",\n \"############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\"\",\n \"\\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back\",\n \"== 0: LOG(\\\"OK\\\") # send the OK response back to the TECO retString\",\n \"data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 ############# # functions #\",\n \"or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back to\",\n \"the TCP server LOG(\\\"Open the TCP server\\\") createServer() # start the tasks LOG(\\\"start\",\n \"UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open\",\n \"0: LOG(\\\"OK\\\") # send the OK response back to the TECO retString =\",\n \"0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system\",\n \"def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1)\",\n \"the implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\",\n \"\\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" #\",\n \"the MIT License as published by the * # Massachusetts Institute of Technology.\",\n \"pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the OK response\",\n \"LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString = \\\"Error:\",\n \"\\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise the console handler consoleHandler\",\n \"= self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input:\",\n \"more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS\",\n \"response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client\",\n \"socket data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected\",\n \"* # The Space Python Library is free software; you can redistribute it\",\n \"(C) 2018, <NAME>, Austria * # * # The Space Python Library is\",\n \"line has to be handled in a special way and can not be\",\n \"line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the client connection if exit\",\n \"read the next set of byte from the data socket data = self.recv(LINEBUFFERLEN)\",\n \"input: extract the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the\",\n \"way and can not be # processed directly lastLine = lines[-1] lines =\",\n \"self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString\",\n \"FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * # for more\",\n \"############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes # ###########\",\n \"# * # The Space Python Library is free software; you can redistribute\",\n \"= \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has\",\n \"self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") #\",\n \"= UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server\",\n \"was complete (incl. \\\"\\\\n\\\") pass else: # last line was cutt off and\",\n \"from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP #############\",\n \"PARTICULAR PURPOSE. See the MIT License * # for more details. * #******************************************************************************\",\n \"import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants\",\n \"if data == None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer\",\n \"failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client\",\n \"the system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() #\",\n \"UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes\",\n \"automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer)\",\n \"register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP\",\n \"\\\" + line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def\",\n \"LOG(\\\"--- idle ---\\\") ######## # main # ######## if __name__ == \\\"__main__\\\": #\",\n \"# read the next set of byte from the data socket data =\",\n \"\\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] #\",\n \"\\\"\\\": # read of the data was complete (incl. \\\"\\\\n\\\") pass else: #\",\n \"# create the TCP server LOG(\\\"Open the TCP server\\\") createServer() # start the\",\n \"\\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate\",\n \"UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask):\",\n \"WARRANTY; without even the implied warranty of * # MERCHANTABILITY or FITNESS FOR\",\n \"the next read self.tcpLineBuffer = lastLine for line in lines: # remove a\",\n \"+= data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract the lines\",\n \"# the last line has to be handled in a special way and\",\n \"(case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"):\",\n \"# The Space Python Library is distributed in the hope that it will\",\n \"the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"):\",\n \"retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to\",\n \"data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract the lines from\",\n \"----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ########\",\n \"Error response back to the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\"\",\n \"cutt off and the rest should come with the next read self.tcpLineBuffer =\",\n \"when a client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line)\",\n \"256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\"\",\n \"remove a terminating \\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\": line\",\n \"connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus ==\",\n \"read self.tcpLineBuffer = lastLine for line in lines: # remove a terminating \\\"\\\\r\\\"\",\n \"system configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise\",\n \"even the implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR\",\n \"client connection if exit has been entered (case insensitive) upperLine = line.upper() if\",\n \"***\\\") # read the next set of byte from the data socket data\",\n \"def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback\",\n \"processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": #\",\n \"ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY or FITNESS\",\n \"clients like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the\",\n \"details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import\",\n \"self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract\",\n \"be handled in a special way and can not be # processed directly\",\n \"self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or\",\n \"PURPOSE. See the MIT License * # for more details. * #****************************************************************************** #\",\n \"processLine(self, line): \\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\" LOG(\\\"line =\",\n \"UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler\",\n \"the MIT License * # for more details. * #****************************************************************************** # Unit Tests\",\n \"= lines[:-1] if lastLine == \\\"\\\": # read of the data was complete\",\n \"# remove a terminating \\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\":\",\n \"line) return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise\",\n \"the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if\",\n \"= self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return tcpLineBuffer\",\n \"# ######## if __name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration() #\",\n \"classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def\",\n \"the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create\",\n \"a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 ############# # functions\",\n \"can not be # processed directly lastLine = lines[-1] lines = lines[:-1] if\",\n \"retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line):\",\n \"= line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") #\",\n \"self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return tcpLineBuffer =\",\n \"the rest should come with the next read self.tcpLineBuffer = lastLine for line\",\n \"without even the implied warranty of * # MERCHANTABILITY or FITNESS FOR A\",\n \"(incl. \\\"\\\\n\\\") pass else: # last line was cutt off and the rest\",\n \"server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function\",\n \"of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask\",\n \"useful, * # but WITHOUT ANY WARRANTY; without even the implied warranty of\",\n \"client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if\",\n \"be # processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine ==\",\n \"data was complete (incl. \\\"\\\\n\\\") pass else: # last line was cutt off\",\n \"LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # #############\",\n \"input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the OK\",\n \"* # Massachusetts Institute of Technology. * # * # The Space Python\",\n \"receiveCallback ***\\\") # read the next set of byte from the data socket\",\n \"# client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer:\",\n \"# initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask\",\n \"console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) #\",\n \"# --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has send a data\",\n \"for clients like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate\",\n \"Institute of Technology. * # * # The Space Python Library is distributed\",\n \"Massachusetts Institute of Technology. * # * # The Space Python Library is\",\n \"if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the client connection if\",\n \"implied warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See\",\n \"Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants #\",\n \"terminating \\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1]\",\n \"stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read\",\n \"constants # ############# LINEBUFFERLEN = 256 ########### # classes # ########### # =============================================================================\",\n \"= TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() #\",\n \"software; you can redistribute it and/or * # modify it under under the\",\n \"License as published by the * # Massachusetts Institute of Technology. * #\",\n \"handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register\",\n \"self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client\",\n \"* # * # The Space Python Library is free software; you can\",\n \"== \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response\",\n \"Library is free software; you can redistribute it and/or * # modify it\",\n \"UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### #\",\n \"\\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\")\",\n \"for line in lines: # remove a terminating \\\"\\\\r\\\" for clients like telnet\",\n \"__init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer =\",\n \"return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send\",\n \"connection if exit has been entered (case insensitive) upperLine = line.upper() if (upperLine\",\n \"been entered (case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine\",\n \"\\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer():\",\n \"client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) #\",\n \"# initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler)\",\n \"response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set\",\n \"send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 ############# #\",\n \"delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send\",\n \"Technology. * # * # The Space Python Library is distributed in the\",\n \"modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP server\\\") createServer() # start\",\n \"back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection\",\n \"(upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back to the\",\n \"== \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response\",\n \"pass else: # last line was cutt off and the rest should come\",\n \"you can redistribute it and/or * # modify it under under the terms\",\n \"--------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\"\",\n \"* # for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import\",\n \"data socket data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically\",\n \"return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle\",\n \"* # The Space Python Library is distributed in the hope that it\",\n \"# read of the data was complete (incl. \\\"\\\\n\\\") pass else: # last\",\n \"terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line);\",\n \"retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate\",\n \"TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # -----------------------------------------------------------------------------\",\n \"the hope that it will be useful, * # but WITHOUT ANY WARRANTY;\",\n \"line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled\",\n \"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License *\",\n \"Library is distributed in the hope that it will be useful, * #\",\n \"\\\" + tcpLineBuffer) # handle the input: extract the lines from the line\",\n \"= \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine ==\",\n \"of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT\",\n \"handle the input: extract the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\")\",\n \"= UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket,\",\n \"self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus\",\n \"# terminate the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine\",\n \"should come with the next read self.tcpLineBuffer = lastLine for line in lines:\",\n \"TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back\",\n \"modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP\",\n \"########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr):\",\n \"come with the next read self.tcpLineBuffer = lastLine for line in lines: #\",\n \"----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) #\",\n \"the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True)\",\n \"# terminate the client connection if exit has been entered (case insensitive) upperLine\",\n \"============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes\",\n \"* #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error,\",\n \"the OK response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus))\",\n \"special way and can not be # processed directly lastLine = lines[-1] lines\",\n \"UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback\",\n \"like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the client\",\n \"createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) #\",\n \"the input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the\",\n \"+ tcpLineBuffer) # handle the input: extract the lines from the line buffer\",\n \"initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def\",\n \"# for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys\",\n \"back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the\",\n \"UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ######## if __name__ ==\",\n \"if pstatus == 0: LOG(\\\"OK\\\") # send the OK response back to the\",\n \"# ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise\",\n \"self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus == 0:\",\n \"__name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise the console\",\n \"telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the client connection\",\n \"hope that it will be useful, * # but WITHOUT ANY WARRANTY; without\",\n \"# ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]])\",\n \"# Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO,\",\n \"the input: extract the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") #\",\n \"server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000,\",\n \"LOG(\\\"Quit requested\\\") # send the OK response back to the client retString =\",\n \"# ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main #\",\n \"byte from the data socket data = self.recv(LINEBUFFERLEN) if data == None: #\",\n \"or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * # for\",\n \"the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0)\",\n \"socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") #\",\n \"portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a\",\n \"from the data socket data = self.recv(LINEBUFFERLEN) if data == None: # client\",\n \"data == None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer\",\n \"server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction()\",\n \"--------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"***\",\n \"== \\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise the console handler\",\n \"of Technology. * # * # The Space Python Library is distributed in\",\n \"was cutt off and the rest should come with the next read self.tcpLineBuffer\",\n \"WITHOUT ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY or\",\n \"2018, <NAME>, Austria * # * # The Space Python Library is free\",\n \"# but WITHOUT ANY WARRANTY; without even the implied warranty of * #\",\n \"# main # ######## if __name__ == \\\"__main__\\\": # initialise the system configuration\",\n \"\\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when\",\n \"the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def\",\n \"# modify it under under the terms of the MIT License as published\",\n \"######## # main # ######## if __name__ == \\\"__main__\\\": # initialise the system\",\n \"(upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK\",\n \"a client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return\",\n \"# --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\"\",\n \"the OK response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate\",\n \"to the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # ---------------------------------------------------------------------------\",\n \"LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN\",\n \"LOG(\\\"Exit requested\\\") # send the OK response back to the client retString =\",\n \"lastLine == \\\"\\\": # read of the data was complete (incl. \\\"\\\\n\\\") pass\",\n \"# ############# LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class\",\n \"if lastLine == \\\"\\\": # read of the data was complete (incl. \\\"\\\\n\\\")\",\n \"idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ######## if __name__\",\n \"line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 ############# # functions # #############\",\n \"\\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input\",\n \"has been entered (case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or\",\n \"lastLine = lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": # read of\",\n \"# send the OK response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode())\",\n \"# register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the\",\n \"set of byte from the data socket data = self.recv(LINEBUFFERLEN) if data ==\",\n \"when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next\",\n \"rest should come with the next read self.tcpLineBuffer = lastLine for line in\",\n \"#****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS,\",\n \"the Error response back to the client: retString = \\\"Error: execution failed (see\",\n \"class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\"\",\n \"TCP server LOG(\\\"Open the TCP server\\\") createServer() # start the tasks LOG(\\\"start modelTask...\\\")\",\n \"Austria * # * # The Space Python Library is free software; you\",\n \"tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled in a special way\",\n \"# ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not\",\n \"def processLine(self, line): \\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\" LOG(\\\"line\",\n \"LINEBUFFERLEN = 256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass\",\n \"line[:-1] # terminate the client connection if exit has been entered (case insensitive)\",\n \"def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ######## if\",\n \"\\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send\",\n \"######## if __name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise\",\n \"LOG(\\\"OK\\\") # send the OK response back to the TECO retString = \\\"OK\\\\n\\\";\",\n \"published by the * # Massachusetts Institute of Technology. * # * #\",\n \"main # ######## if __name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration()\",\n \"to be handled in a special way and can not be # processed\",\n \"configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the\",\n \"has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set of byte\",\n \"* # but WITHOUT ANY WARRANTY; without even the implied warranty of *\",\n \"last line was cutt off and the rest should come with the next\",\n \"send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set of byte from\",\n \"buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled in\",\n \"OK response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) #\",\n \"(upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK\",\n \"else: LOG_ERROR(str(pstatus)) # set the Error response back to the client: retString =\",\n \"response back to the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode())\",\n \"lines: # remove a terminating \\\"\\\\r\\\" for clients like telnet if line[-1] ==\",\n \"(upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back to the\",\n \"lines[:-1] if lastLine == \\\"\\\": # read of the data was complete (incl.\",\n \"= lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": # read of the\",\n \"connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\")\",\n \"Python Library is distributed in the hope that it will be useful, *\",\n \"that it will be useful, * # but WITHOUT ANY WARRANTY; without even\",\n \"############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\",\",\n \"the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create\",\n \"idle ---\\\") ######## # main # ######## if __name__ == \\\"__main__\\\": # initialise\",\n \"activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle\",\n \"send the OK response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else:\",\n \"\\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" +\",\n \"distributed in the hope that it will be useful, * # but WITHOUT\",\n \"# initialise the system configuration initConfiguration() # initialise the console handler consoleHandler =\",\n \"back to the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) #\",\n \"lastLine for line in lines: # remove a terminating \\\"\\\\r\\\" for clients like\",\n \"the client connection if exit has been entered (case insensitive) upperLine = line.upper()\",\n \"MIT License * # for more details. * #****************************************************************************** # Unit Tests *\",\n \"Space Python Library is free software; you can redistribute it and/or * #\",\n \"by the * # Massachusetts Institute of Technology. * # * # The\",\n \"if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the\",\n \"self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the OK response back to\",\n \"has to be handled in a special way and can not be #\",\n \"lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has\",\n \"is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" +\",\n \"can redistribute it and/or * # modify it under under the terms of\",\n \"# handle the input: extract the lines from the line buffer lines =\",\n \"return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the\",\n \"but WITHOUT ANY WARRANTY; without even the implied warranty of * # MERCHANTABILITY\",\n \"# activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"---\",\n \"only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def\",\n \"line in lines: # remove a terminating \\\"\\\\r\\\" for clients like telnet if\",\n \"under under the terms of the MIT License as published by the *\",\n \"zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\")\",\n \"line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send\",\n \"is distributed in the hope that it will be useful, * # but\",\n \"lines = lines[:-1] if lastLine == \\\"\\\": # read of the data was\",\n \"sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP\",\n \"the last line has to be handled in a special way and can\",\n \"the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return\",\n \"it will be useful, * # but WITHOUT ANY WARRANTY; without even the\",\n \"Python Library is free software; you can redistribute it and/or * # modify\",\n \"# constants # ############# LINEBUFFERLEN = 256 ########### # classes # ########### #\",\n \"== \\\"\\\": # read of the data was complete (incl. \\\"\\\\n\\\") pass else:\",\n \"self.tcpLineBuffer = lastLine for line in lines: # remove a terminating \\\"\\\\r\\\" for\",\n \"= UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console\",\n \"* # modify it under under the terms of the MIT License as\",\n \"idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ######## if __name__ == \\\"__main__\\\":\",\n \"tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the\",\n \"last line has to be handled in a special way and can not\",\n \"terms of the MIT License as published by the * # Massachusetts Institute\",\n \"See the MIT License * # for more details. * #****************************************************************************** # Unit\",\n \"the next set of byte from the data socket data = self.recv(LINEBUFFERLEN) if\",\n \"line was cutt off and the rest should come with the next read\",\n \"execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a\",\n \"warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\",\n \"terminate the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine ==\",\n \"be useful, * # but WITHOUT ANY WARRANTY; without even the implied warranty\",\n \"client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit\",\n \"function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## #\",\n \"#****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG,\",\n \"A PARTICULAR PURPOSE. See the MIT License * # for more details. *\",\n \"off and the rest should come with the next read self.tcpLineBuffer = lastLine\",\n \"a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set\",\n \"self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has send a\",\n \"# The Space Python Library is free software; you can redistribute it and/or\",\n \"Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING,\",\n \"initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model\",\n \"to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error\",\n \"read of the data was complete (incl. \\\"\\\\n\\\") pass else: # last line\",\n \"data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set of byte from the\",\n \"and can not be # processed directly lastLine = lines[-1] lines = lines[:-1]\",\n \"import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ###########\",\n \"it and/or * # modify it under under the terms of the MIT\",\n \"the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line\",\n \"else: # last line was cutt off and the rest should come with\",\n \"= \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the\",\n \"Space Python Library is distributed in the hope that it will be useful,\",\n \"#****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The Space Python\",\n \"########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" #\",\n \"import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK,\",\n \"--------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr)\",\n \"TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle\",\n \"if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the\",\n \"to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient();\",\n \"UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ########### # classes #\",\n \"idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ########\",\n \"not be # processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine\",\n \"in lines: # remove a terminating \\\"\\\\r\\\" for clients like telnet if line[-1]\",\n \"retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine\",\n \"sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction)\",\n \"it under under the terms of the MIT License as published by the\",\n \"# Massachusetts Institute of Technology. * # * # The Space Python Library\",\n \"UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self,\",\n \"a terminating \\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\": line =\",\n \"# set the Error response back to the client: retString = \\\"Error: execution\",\n \"# ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self,\",\n \"tcpLineBuffer) # handle the input: extract the lines from the line buffer lines\",\n \"def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # -----------------------------------------------------------------------------\",\n \"FOR A PARTICULAR PURPOSE. See the MIT License * # for more details.\",\n \"License * # for more details. * #****************************************************************************** # Unit Tests * #******************************************************************************\",\n \"\\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask =\",\n \"[\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server\",\n \"# (C) 2018, <NAME>, Austria * # * # The Space Python Library\",\n \"attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # ---------------------------------------------------------------------------\",\n \"LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256\",\n \"client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set of\",\n \"client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0\",\n \"the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response\",\n \"console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP server\\\") createServer()\",\n \"initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) #\",\n \"in the hope that it will be useful, * # but WITHOUT ANY\",\n \"the data was complete (incl. \\\"\\\\n\\\") pass else: # last line was cutt\",\n \"* # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License\",\n \"MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License * #\",\n \"a special way and can not be # processed directly lastLine = lines[-1]\",\n \"data = self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected return\",\n \"modify it under under the terms of the MIT License as published by\",\n \"modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when\",\n \"UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\"\",\n \"modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self,\",\n \"== None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer +=\",\n \"upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\")\",\n \"= line[:-1] # terminate the client connection if exit has been entered (case\",\n \"if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def\",\n \"#!/usr/bin/env python3 #****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The\",\n \"LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN =\",\n \"# processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine == \\\"\\\":\",\n \"\\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if\",\n \"if __name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise the\",\n \"* # * # The Space Python Library is distributed in the hope\",\n \"model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the\",\n \"handled in a special way and can not be # processed directly lastLine\",\n \"The Space Python Library is distributed in the hope that it will be\",\n \"for more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from\",\n \"---\\\") ######## # main # ######## if __name__ == \\\"__main__\\\": # initialise the\",\n \"extract the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last\",\n \"python3 #****************************************************************************** # (C) 2018, <NAME>, Austria * # * # The Space\",\n \"client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self,\",\n \"disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) #\",\n \"of the data was complete (incl. \\\"\\\\n\\\") pass else: # last line was\",\n \"portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\"\",\n \"\\\"\\\\r\\\": line = line[:-1] # terminate the client connection if exit has been\",\n \"MIT License as published by the * # Massachusetts Institute of Technology. *\",\n \"set the Error response back to the client: retString = \\\"Error: execution failed\",\n \"redistribute it and/or * # modify it under under the terms of the\",\n \"\\\"\\\\n\\\") pass else: # last line was cutt off and the rest should\",\n \"client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus\",\n \"\\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to the client:\",\n \"lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": # read of the data\",\n \"log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has send\",\n \"or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back to\",\n \"the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP server\\\")\",\n \"of byte from the data socket data = self.recv(LINEBUFFERLEN) if data == None:\",\n \"consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the\",\n \"----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST):\",\n \"if exit has been entered (case insensitive) upperLine = line.upper() if (upperLine ==\",\n \"insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit\",\n \"== \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back to the client\",\n \"== \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back to the client\",\n \"# classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # ---------------------------------------------------------------------------\",\n \"tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract the\",\n \"TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask\",\n \"the terms of the MIT License as published by the * # Massachusetts\",\n \"the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be\",\n \"and/or * # modify it under under the terms of the MIT License\",\n \"# ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"],\",\n \"sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\")\",\n \"[\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT))\",\n \"as published by the * # Massachusetts Institute of Technology. * # *\",\n \"# --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask,\",\n \"== \\\"\\\\r\\\": line = line[:-1] # terminate the client connection if exit has\",\n \"configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP\",\n \"terminate the client connection if exit has been entered (case insensitive) upperLine =\",\n \"# delegate the input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") #\"\n]","isConversational":false}}},{"rowIdx":3484,"cells":{"ngram":{"kind":"list like","value":["override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), )","def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value',","cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj',","pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct","validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try:","= validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected","test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if this schema has any","'<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar',","999 is required but version {} is ' 'installed. Perhaps run `pip install","True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA)","a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a","'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping',","{'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct)","{'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with","MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==","'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo',","logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag","'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( #","cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At key:","not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'),","test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj","@pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml')","'<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern","CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if this schema","args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo:","'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ), )","def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{","validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev:","ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ),","item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in","= tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), (","tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog):","'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), ( { 'repos':","def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes():","'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret","from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from","), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id':","not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo',","@pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not a string","to set a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity',","DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'}","False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), (","CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION","test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n'","as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg","'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta',","isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set","def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg,","# Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'],","test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA)","r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import","= [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys","from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def","fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,))","( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, #","import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema):","test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def","pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib","MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist","[{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', #","import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from",") def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha':","hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be","pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema)","'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples","dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':","f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n'","( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def","way our merging works, if this schema has any defaults they will clobber","--upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'}","'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes',","logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')","}], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert","'999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At","test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj","= {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys","'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ),","'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo,","CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert","CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')}","== {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid","'mapping', ( # invalid language key {'pony': '1.0'}, # not a string for","dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':","), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is","- id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val","'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True,","return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'),","['foo', 'bar', 'baz'], }, ], }], }, True, ), ( { 'repos': [{","not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj',","import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import","test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', (","[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True,","At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version {} is","in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items","clobber potentially useful values in the backing manifest. #227 \"\"\" for item in","'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( # A regression in 0.13.5:","import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import","expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo':","MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config","is required but version {} is ' 'installed. Perhaps run `pip install --upgrade","validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert",") def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def","unicode_literals import logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib","# A regression in 0.13.5: always_run and files are permissible [{ 'id': 'a',","CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n'","test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',","useful values in the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert","def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if this schema has","pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib","install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version':","has any defaults they will clobber potentially useful values in the backing manifest.","' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,))","test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony':","required but version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format(","must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def","'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude","this schema has any defaults they will clobber potentially useful values in the","}, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [","from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from","{ 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], },","'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), (","[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main))",") ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit',","('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( {","expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo':","}], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':","regression in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b',","def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the","), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a',","assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a',","'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both():","'\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',","isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def","False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( #","hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert","cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not","True, ), ( # A regression in 0.13.5: always_run and files are permissible","both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'},","cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib","with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert","(CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item,","{'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize(","expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def","not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert","'1.0'}, # not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping):","pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [],","as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) ==","'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ),","language key {'pony': '1.0'}, # not a string for version {'python': 3}, ),","{'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError):","'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( { 'repos': [{","'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo:","[{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo':","cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema):","== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main,","'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo':","] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema =","import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from","item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def","validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo:","assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s):","'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos':","'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta","True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret","tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( (","'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct","a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False,","assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry':","#227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert","assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not","# name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]},","CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to","from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from","[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True,","import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import","config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj =","'key')} warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ]","Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version","import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return","'files': '\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev':","MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo':","), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a',","def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional,","flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples","'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==>","'\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), ( {","f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f","{item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for","assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ]","invalid language key {'pony': '1.0'}, # not a string for version {'python': 3},","' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing():","{'pony': '1.0'}, # not a string for version {'python': 3}, ), ) def","'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', (","' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==","key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '-","'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ), ( [{ 'id':","invalid to set a language for a meta hook {'repo': 'meta', 'hooks': [{'id':","0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ), )","string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ),","'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct","the way our merging works, if this schema has any defaults they will","test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,))","pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ],","not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}')","\"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not","' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n',","msg, = excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize(","specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha':","'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct)","( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files':","https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n',","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ),","'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg","}, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret =","Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg =","version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing():","potentially useful values in the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items:","pre-commit version 999 is required but version {} is ' 'installed. Perhaps run","'bar', 'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected):","be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], },","caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def","( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'},","{'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a","'\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is","[], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key","'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct):","from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from","], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',","try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def","{ 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files':","def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT))","'- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' '","test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, =","'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ),","from __future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants as","repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args:","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True,","pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures","in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry':","check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA","assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat',","'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( #","'files': r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',","ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'},","'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha and","'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta',","( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }],","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be","config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml =","'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n'","test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks:","excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg ==","[{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '-","caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' '","C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT","works, if this schema has any defaults they will clobber potentially useful values","[--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (","), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4',","and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta',","('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',","import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import","Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], },","is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA)","name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ),","import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import","fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',","= {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging","( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files':","MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main","# i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid","def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b',","id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert","def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':","identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo:","caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n'","' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not","'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def","hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys)","`pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [],","[], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n'","will clobber potentially useful values in the backing manifest. #227 \"\"\" for item","DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main","be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo):","= is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(),","{ 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a string 'exclude':",") def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo:","from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except","}], }, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA)","pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib","import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import","'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ), ( [{","for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def","CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not a","), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n'","test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev':","def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg,","with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert","['foo', 'bar', 'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj,","'files': r'\\.py$', }], True, ), ( # A regression in 0.13.5: always_run and","= excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct',","expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj =","check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',","hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(","str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit","' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', )","'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo':","for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',))","obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel'))","from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from","= is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo':","'python3.4', 'files': r'\\.py$', }], True, ), ( # A regression in 0.13.5: always_run","test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' '","'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ), ( [{ 'id': 'a',","'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ),","must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }],","('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id':","= tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir,","'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ),","'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret","3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val =","'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], },","'python', 'files': r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry':","'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with","ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos':","'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ),","ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ),","'bar', 'baz'], }, ], }], }, True, ), ( { 'repos': [{ 'repo':","{'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError)","valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language","cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in","string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION)","'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), (","'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), )","pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, #","# not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with","'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta hook","excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == (","{repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f =","== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir,","def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':","warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert","validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert","( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), )","At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but","'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha and rev'","[{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize(","return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError):","), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid","'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==","'==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required","foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{')","r'\\.py$', }], True, ), ( # A regression in 0.13.5: always_run and files","'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo',","), ( # A regression in 0.13.5: always_run and files are permissible [{","'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a string 'exclude': 0,","}, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert","cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version':","cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n'","'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify","pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT","not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('-","= {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos':","), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected","[{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks':","import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import","'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha","'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ],","{'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys =","with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks',","language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, #","'==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version {}","{'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with","id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val","'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def","any defaults they will clobber potentially useful values in the backing manifest. #227","'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a","'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct):",") def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA,","caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn',","excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', (","(validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}')","hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for","is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag',","[ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }],","fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a',","'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks',","from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from","not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{","a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]},","pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert","[ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys ==","= {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==>","' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==","cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir):","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True,","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'],","'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name':","), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is","{'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At","), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected","the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional)","'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo':","tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize(","assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ]","meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')","test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo':","'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo':","'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]},","- id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not","'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args':","{} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) )","string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with","CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with","'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj,","True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value)","{'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ),","'files': '\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args': ['foo',","{'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook","( # invalid language key {'pony': '1.0'}, # not a string for version","testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError:","), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema","tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' -","cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due","they will clobber potentially useful values in the backing manifest. #227 \"\"\" for","test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg =","'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir,","from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from","def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg","'language': 'python', 'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj,","meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must","def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key","test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }],","def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct',","'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev':","CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items","to the way our merging works, if this schema has any defaults they","pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg,","files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':","logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn):","'\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar',","pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev':","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }],","cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value):","assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id':","__future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants as C","import unicode_literals import logging import cfgv import pytest import pre_commit.constants as C from","'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( # A regression","assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo':","{'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works,","permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run':","@pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'},","{ 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }],","[sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if","}, ], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev':","True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ {","flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert","a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override","( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$',","validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config","rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', )","'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity',","1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (","minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version {} is ' 'installed.","schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if","'0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item","pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults():","id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [","'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError):","{'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def","assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't","[{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True,","{'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def","are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '',","'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f =","CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA","is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'},","in the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item,","ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING,","if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if isinstance(x,","manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok():","key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version {} is '","'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct =","'=====> pre-commit version 999 is required but version {} is ' 'installed. Perhaps","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ), (","with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'},","'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':","assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====>","config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our","False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret","import logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import","assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id:","' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert","@pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat',","not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError):","C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA)","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( { 'repos':","test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, =","fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b',","test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way","test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct)","MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot","'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok():","== 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo':","'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT)","not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' '","'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for","if this schema has any defaults they will clobber potentially useful values in","], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj,","config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n'","with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args","'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta',","'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', (","CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev","our merging works, if this schema has any defaults they will clobber potentially","MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo':","'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks':","hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,))","= {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x","[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$',","# invalid language key {'pony': '1.0'}, # not a string for version {'python':","import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import","https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id:","and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',","= tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' -","'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ),","version 999 is required but version {} is ' 'installed. Perhaps run `pip","= {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}","[{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta hook {'repo':","= tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,))","( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999","'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), (","f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def",") def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize(","'i-dont-exist'}]}, # invalid to set a language for a meta hook {'repo': 'meta',","pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib","pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj,","( # A regression in 0.13.5: always_run and files are permissible [{ 'id':","import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return","with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos':","except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize(","is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( {","'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as","defaults they will clobber potentially useful values in the backing manifest. #227 \"\"\"","'python', 'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected):","{'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct)","[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog):","3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val","set a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language':","] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' '","'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( # A regression in","'language': 'python', 'files': r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b',","True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version':","'- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n'","[{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args':","= {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}","( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes',","{'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must","def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta,","validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True","( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f","in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok():","rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev':","'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct","@pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id':","f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id:","@pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args',","pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib","{'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default():","def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'}","}], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], },","dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev':","version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, )","values in the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not","'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ), ) def","sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False","from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj,","def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n'","with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def","backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def","tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id:","assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name':","pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value)","allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [","'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id':","@pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':","def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails():","== {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'}",") def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha':","assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml')","def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev:","def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not","repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' '","'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both","ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha':","== ( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version","msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'},","assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError):","'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ), ) def","= tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' '","obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with","}], True, ), ( # A regression in 0.13.5: always_run and files are","test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',","@pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if","assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n'","def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',))","rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val","'', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret =","for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x","but version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION,","key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml')","args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [","3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError)","rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA)","\"\"\"Due to the way our merging works, if this schema has any defaults","a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping,","run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos':","f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' '","schema has any defaults they will clobber potentially useful values in the backing","<reponame>yoavcaspi/pre-commit from __future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants","merging works, if this schema has any defaults they will clobber potentially useful","test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f =",") ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema',","not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',))","'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert","x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys == set(warn_additional.keys)","), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as","dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a","ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj,","pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib","is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]}","' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val =","[dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj,","'baz'], }, ], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks',","f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n'","key {'pony': '1.0'}, # not a string for version {'python': 3}, ), )","'c', 'language': 'python', 'files': r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name':","{'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]}","sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo':","# invalid to set a language for a meta hook {'repo': 'meta', 'hooks':","cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if this","'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct =","'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml","( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}],","'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( # A","i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to","A regression in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name':","pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib","0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c',","MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'})","'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(","{'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string",") def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language","for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name","def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f","@pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( (","[ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a string","always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$',","is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'},"],"string":"[\n \"override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), )\",\n \"def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value',\",\n \"cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj',\",\n \"pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct\",\n \"validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try:\",\n \"= validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected\",\n \"test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if this schema has any\",\n \"'<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar',\",\n \"999 is required but version {} is ' 'installed. Perhaps run `pip install\",\n \"True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA)\",\n \"a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a\",\n \"'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping',\",\n \"{'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct)\",\n \"{'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with\",\n \"MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==\",\n \"'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo',\",\n \"logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag\",\n \"'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( #\",\n \"cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At key:\",\n \"not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'),\",\n \"test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj\",\n \"@pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml')\",\n \"'<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern\",\n \"CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if this schema\",\n \"args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo:\",\n \"'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ), )\",\n \"def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{\",\n \"validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev:\",\n \"ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ),\",\n \"item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in\",\n \"= tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), (\",\n \"tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog):\",\n \"'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), ( { 'repos':\",\n \"def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes():\",\n \"'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret\",\n \"from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from\",\n \"), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id':\",\n \"not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo',\",\n \"@pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not a string\",\n \"to set a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity',\",\n \"DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'}\",\n \"False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), (\",\n \"CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION\",\n \"test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n'\",\n \"as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg\",\n \"'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta',\",\n \"isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set\",\n \"def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg,\",\n \"# Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'],\",\n \"test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA)\",\n \"r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import\",\n \"= [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys\",\n \"from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def\",\n \"fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,))\",\n \"( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, #\",\n \"import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema):\",\n \"test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def\",\n \"pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib\",\n \"MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist\",\n \"[{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', #\",\n \"import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from\",\n \") def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha':\",\n \"hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be\",\n \"pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema)\",\n \"'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples\",\n \"dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':\",\n \"f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n'\",\n \"( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def\",\n \"way our merging works, if this schema has any defaults they will clobber\",\n \"--upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'}\",\n \"'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes',\",\n \"logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')\",\n \"}], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert\",\n \"'999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At\",\n \"test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj\",\n \"= {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys\",\n \"'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ),\",\n \"'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo,\",\n \"CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert\",\n \"CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')}\",\n \"== {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid\",\n \"'mapping', ( # invalid language key {'pony': '1.0'}, # not a string for\",\n \"dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':\",\n \"), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is\",\n \"- id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val\",\n \"'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True,\",\n \"return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'),\",\n \"['foo', 'bar', 'baz'], }, ], }], }, True, ), ( { 'repos': [{\",\n \"not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj',\",\n \"import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import\",\n \"test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', (\",\n \"[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True,\",\n \"At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version {} is\",\n \"in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items\",\n \"clobber potentially useful values in the backing manifest. #227 \\\"\\\"\\\" for item in\",\n \"'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A regression in 0.13.5:\",\n \"import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import\",\n \"expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo':\",\n \"MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config\",\n \"is required but version {} is ' 'installed. Perhaps run `pip install --upgrade\",\n \"validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert\",\n \") def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def\",\n \"unicode_literals import logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib\",\n \"# A regression in 0.13.5: always_run and files are permissible [{ 'id': 'a',\",\n \"CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n'\",\n \"test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',\",\n \"useful values in the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert\",\n \"def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if this schema has\",\n \"pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib\",\n \"install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version':\",\n \"has any defaults they will clobber potentially useful values in the backing manifest.\",\n \"' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,))\",\n \"test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony':\",\n \"required but version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format(\",\n \"must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def\",\n \"'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude\",\n \"this schema has any defaults they will clobber potentially useful values in the\",\n \"}, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [\",\n \"from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from\",\n \"{ 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], },\",\n \"'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), (\",\n \"[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main))\",\n \") ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit',\",\n \"('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( {\",\n \"expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo':\",\n \"}], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':\",\n \"regression in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b',\",\n \"def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the\",\n \"), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a',\",\n \"assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a',\",\n \"'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both():\",\n \"'\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',\",\n \"isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def\",\n \"False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( #\",\n \"hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert\",\n \"cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not\",\n \"True, ), ( # A regression in 0.13.5: always_run and files are permissible\",\n \"both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'},\",\n \"cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib\",\n \"with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert\",\n \"(CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item,\",\n \"{'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize(\",\n \"expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def\",\n \"not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert\",\n \"'1.0'}, # not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping):\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [],\",\n \"as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) ==\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ),\",\n \"language key {'pony': '1.0'}, # not a string for version {'python': 3}, ),\",\n \"{'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError):\",\n \"'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{\",\n \"'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo:\",\n \"[{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo':\",\n \"cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema):\",\n \"== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main,\",\n \"'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo':\",\n \"] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema =\",\n \"import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from\",\n \"item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def\",\n \"validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo:\",\n \"assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s):\",\n \"'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos':\",\n \"'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta\",\n \"True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret\",\n \"tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( (\",\n \"'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct\",\n \"a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False,\",\n \"assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"#227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert\",\n \"assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not\",\n \"# name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]},\",\n \"CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to\",\n \"from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from\",\n \"[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True,\",\n \"import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import\",\n \"config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj =\",\n \"'key')} warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ]\",\n \"Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version\",\n \"import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return\",\n \"'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev':\",\n \"MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo':\",\n \"), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a',\",\n \"def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional,\",\n \"flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples\",\n \"'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==>\",\n \"'\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ), ( {\",\n \"f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f\",\n \"{item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for\",\n \"assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ]\",\n \"invalid language key {'pony': '1.0'}, # not a string for version {'python': 3},\",\n \"' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing():\",\n \"{'pony': '1.0'}, # not a string for version {'python': 3}, ), ) def\",\n \"'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', (\",\n \"' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==\",\n \"key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '-\",\n \"'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id':\",\n \"invalid to set a language for a meta hook {'repo': 'meta', 'hooks': [{'id':\",\n \"0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ), )\",\n \"string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ),\",\n \"'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct\",\n \"the way our merging works, if this schema has any defaults they will\",\n \"test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,))\",\n \"pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ],\",\n \"not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}')\",\n \"\\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not\",\n \"' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n',\",\n \"msg, = excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize(\",\n \"specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha':\",\n \"'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct)\",\n \"( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files':\",\n \"https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n',\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ),\",\n \"'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg\",\n \"}, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret =\",\n \"Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg =\",\n \"version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing():\",\n \"potentially useful values in the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items:\",\n \"pre-commit version 999 is required but version {} is ' 'installed. Perhaps run\",\n \"'bar', 'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected):\",\n \"be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], },\",\n \"caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def\",\n \"( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'},\",\n \"{'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a\",\n \"'\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is\",\n \"[], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key\",\n \"'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct):\",\n \"from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from\",\n \"], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',\",\n \"try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def\",\n \"{ 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files':\",\n \"def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT))\",\n \"'- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' '\",\n \"test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, =\",\n \"'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ),\",\n \"from __future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants as\",\n \"repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args:\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True,\",\n \"pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures\",\n \"in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA\",\n \"assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat',\",\n \"'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( #\",\n \"'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'},\",\n \"'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha and\",\n \"'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta',\",\n \"( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }],\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be\",\n \"config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml =\",\n \"'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n'\",\n \"test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks:\",\n \"excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg ==\",\n \"[{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '-\",\n \"caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' '\",\n \"C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT\",\n \"works, if this schema has any defaults they will clobber potentially useful values\",\n \"[--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (\",\n \"), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4',\",\n \"and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta',\",\n \"('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',\",\n \"import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import\",\n \"Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], },\",\n \"is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA)\",\n \"name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ),\",\n \"import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import\",\n \"fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"= {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging\",\n \"( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files':\",\n \"MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main\",\n \"# i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid\",\n \"def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b',\",\n \"id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert\",\n \"def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':\",\n \"identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo:\",\n \"caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n'\",\n \"' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not\",\n \"'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def\",\n \"hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys)\",\n \"`pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [],\",\n \"[], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n'\",\n \"will clobber potentially useful values in the backing manifest. #227 \\\"\\\"\\\" for item\",\n \"DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main\",\n \"be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo):\",\n \"= is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(),\",\n \"{ 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a string 'exclude':\",\n \") def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo:\",\n \"from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except\",\n \"}], }, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA)\",\n \"pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib\",\n \"import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{\",\n \"for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def\",\n \"CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, # not a\",\n \"), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n'\",\n \"test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev':\",\n \"def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg,\",\n \"with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert\",\n \"['foo', 'bar', 'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj,\",\n \"'files': r'\\\\.py$', }], True, ), ( # A regression in 0.13.5: always_run and\",\n \"= excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct',\",\n \"expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj =\",\n \"check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',\",\n \"hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(\",\n \"str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit\",\n \"' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', )\",\n \"'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo':\",\n \"for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',))\",\n \"obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel'))\",\n \"from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from\",\n \"= is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo':\",\n \"'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A regression in 0.13.5: always_run\",\n \"test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' '\",\n \"'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a',\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ),\",\n \"must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }],\",\n \"('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id':\",\n \"= tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir,\",\n \"'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ),\",\n \"'baz'], }, ], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret\",\n \"3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val =\",\n \"'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], },\",\n \"'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with\",\n \"ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos':\",\n \"'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ),\",\n \"ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ),\",\n \"'bar', 'baz'], }, ], }], }, True, ), ( { 'repos': [{ 'repo':\",\n \"{'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError)\",\n \"valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language\",\n \"cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in\",\n \"string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION)\",\n \"'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), (\",\n \"'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), )\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, #\",\n \"# not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with\",\n \"'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta hook\",\n \"excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == (\",\n \"{repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f =\",\n \"== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir,\",\n \"def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':\",\n \"warn_additional, = [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert\",\n \"validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert\",\n \"( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), )\",\n \"At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but\",\n \"'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha and rev'\",\n \"[{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize(\",\n \"return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError):\",\n \"), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid\",\n \"'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==\",\n \"'==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required\",\n \"foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{')\",\n \"r'\\\\.py$', }], True, ), ( # A regression in 0.13.5: always_run and files\",\n \"'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo',\",\n \"), ( # A regression in 0.13.5: always_run and files are permissible [{\",\n \"'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a string 'exclude': 0,\",\n \"}, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert\",\n \"cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version':\",\n \"cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n'\",\n \"'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify\",\n \"pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT\",\n \"not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('-\",\n \"= {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos':\",\n \"), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected\",\n \"[{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks':\",\n \"import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import\",\n \"'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both sha\",\n \"'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ],\",\n \"{'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys =\",\n \"with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks',\",\n \"language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, #\",\n \"'==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version {}\",\n \"{'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with\",\n \"id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val\",\n \"'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def\",\n \"any defaults they will clobber potentially useful values in the backing manifest. #227\",\n \"'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a\",\n \"'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct):\",\n \") def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA,\",\n \"caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn',\",\n \"excinfo.value.args assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', (\",\n \"(validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}')\",\n \"hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for\",\n \"is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag',\",\n \"[ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }],\",\n \"fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a',\",\n \"'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks',\",\n \"from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from\",\n \"not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{\",\n \"a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]},\",\n \"pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert\",\n \"[ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys ==\",\n \"= {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==>\",\n \"' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==\",\n \"cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir):\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True,\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'],\",\n \"'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name':\",\n \"), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is\",\n \"{'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At\",\n \"), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected\",\n \"the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional)\",\n \"'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo':\",\n \"tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize(\",\n \"assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ]\",\n \"meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')\",\n \"test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo':\",\n \"'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo':\",\n \"'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]},\",\n \"- id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not\",\n \"'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args':\",\n \"{} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) )\",\n \"string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with\",\n \"CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with\",\n \"'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj,\",\n \"True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value)\",\n \"{'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ),\",\n \"'files': '\\\\\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args': ['foo',\",\n \"{'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook\",\n \"( # invalid language key {'pony': '1.0'}, # not a string for version\",\n \"testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError:\",\n \"), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema\",\n \"tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' -\",\n \"cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due\",\n \"they will clobber potentially useful values in the backing manifest. #227 \\\"\\\"\\\" for\",\n \"test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg =\",\n \"'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir,\",\n \"from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from\",\n \"def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg\",\n \"'language': 'python', 'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj,\",\n \"meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must\",\n \"def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key\",\n \"test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }],\",\n \"def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct',\",\n \"'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev':\",\n \"CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items\",\n \"to the way our merging works, if this schema has any defaults they\",\n \"pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg,\",\n \"files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':\",\n \"logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn):\",\n \"'\\\\\\\\.py$', # Exclude pattern must be a string 'exclude': 0, 'args': ['foo', 'bar',\",\n \"pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev':\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }],\",\n \"cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value):\",\n \"assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id':\",\n \"__future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants as C\",\n \"import unicode_literals import logging import cfgv import pytest import pre_commit.constants as C from\",\n \"'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A regression\",\n \"assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo':\",\n \"{'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works,\",\n \"permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run':\",\n \"@pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'},\",\n \"{ 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }],\",\n \"[sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if\",\n \"}, ], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev':\",\n \"True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ {\",\n \"flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert\",\n \"a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override\",\n \"( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$',\",\n \"validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config\",\n \"rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', )\",\n \"'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity',\",\n \"1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (\",\n \"minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version {} is ' 'installed.\",\n \"schema.items if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if\",\n \"'0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults():\",\n \"id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [\",\n \"'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError):\",\n \"{'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def\",\n \"assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't\",\n \"[{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True,\",\n \"{'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def\",\n \"are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '',\",\n \"'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f =\",\n \"CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA\",\n \"is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'},\",\n \"in the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item,\",\n \"ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING,\",\n \"if hasattr(item, 'key')} warn_additional, = [ x for x in schema.items if isinstance(x,\",\n \"manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok():\",\n \"key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version {} is '\",\n \"'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct =\",\n \"'=====> pre-commit version 999 is required but version {} is ' 'installed. Perhaps\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), (\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'},\",\n \"'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':\",\n \"assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====>\",\n \"config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our\",\n \"False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret\",\n \"import logging import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import\",\n \"assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id:\",\n \"' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert\",\n \"@pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat',\",\n \"not a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError):\",\n \"C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA)\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos':\",\n \"test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, =\",\n \"fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b',\",\n \"test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way\",\n \"test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct)\",\n \"MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot\",\n \"'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok():\",\n \"== 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo':\",\n \"'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT)\",\n \"not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' '\",\n \"'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for\",\n \"if this schema has any defaults they will clobber potentially useful values in\",\n \"], }], }, False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj,\",\n \"config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n'\",\n \"with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args\",\n \"'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta',\",\n \"'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', (\",\n \"CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev\",\n \"our merging works, if this schema has any defaults they will clobber potentially\",\n \"MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo':\",\n \"'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks':\",\n \"hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,))\",\n \"= {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x\",\n \"[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$',\",\n \"# invalid language key {'pony': '1.0'}, # not a string for version {'python':\",\n \"import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import\",\n \"https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id:\",\n \"and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',\",\n \"= tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' -\",\n \"'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ),\",\n \"version 999 is required but version {} is ' 'installed. Perhaps run `pip\",\n \"= {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}\",\n \"[{'id': 'i-dont-exist'}]}, # invalid to set a language for a meta hook {'repo':\",\n \"= tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,))\",\n \"( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999\",\n \"'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), (\",\n \"f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def\",\n \") def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize(\",\n \"'i-dont-exist'}]}, # invalid to set a language for a meta hook {'repo': 'meta',\",\n \"pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib\",\n \"pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj,\",\n \"( # A regression in 0.13.5: always_run and files are permissible [{ 'id':\",\n \"import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos':\",\n \"except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize(\",\n \"is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( {\",\n \"'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as\",\n \"defaults they will clobber potentially useful values in the backing manifest. #227 \\\"\\\"\\\"\",\n \"'python', 'files': '', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected):\",\n \"{'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct)\",\n \"[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog):\",\n \"3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val\",\n \"set a language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language':\",\n \"] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' '\",\n \"'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A regression in\",\n \"'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b',\",\n \"True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version':\",\n \"'- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n'\",\n \"[{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args':\",\n \"= {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}\",\n \"( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes',\",\n \"{'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must\",\n \"def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta,\",\n \"validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True\",\n \"( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f\",\n \"in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok():\",\n \"rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev':\",\n \"'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct\",\n \"@pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id':\",\n \"f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id:\",\n \"@pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args',\",\n \"pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib\",\n \"{'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default():\",\n \"def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'}\",\n \"}], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], },\",\n \"dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev':\",\n \"version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, )\",\n \"values in the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not\",\n \"'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ), ), ) def\",\n \"sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False\",\n \"from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj,\",\n \"def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n'\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def\",\n \"backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def\",\n \"tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id:\",\n \"assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name':\",\n \"pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value)\",\n \"allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')} warn_additional, = [\",\n \"'identity', 'language': 'python'}]}, # name override must be string {'repo': 'meta', 'hooks': [{'id':\",\n \"@pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':\",\n \"def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails():\",\n \"== {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'}\",\n \") def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha':\",\n \"assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml')\",\n \"def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev:\",\n \"def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not\",\n \"repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' '\",\n \"'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both\",\n \"ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha':\",\n \"== ( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version\",\n \"msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'},\",\n \"assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError):\",\n \"'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ), ), ) def\",\n \"= tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' '\",\n \"obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with\",\n \"}], True, ), ( # A regression in 0.13.5: always_run and files are\",\n \"test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',\",\n \"@pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if\",\n \"assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n'\",\n \"def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',))\",\n \"rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val\",\n \"'', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret =\",\n \"for item in schema.items if hasattr(item, 'key')} warn_additional, = [ x for x\",\n \"but version {} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION,\",\n \"key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml')\",\n \"args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [\",\n \"3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError)\",\n \"rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA)\",\n \"\\\"\\\"\\\"Due to the way our merging works, if this schema has any defaults\",\n \"a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping,\",\n \"run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos':\",\n \"f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' '\",\n \"schema has any defaults they will clobber potentially useful values in the backing\",\n \"<reponame>yoavcaspi/pre-commit from __future__ import unicode_literals import logging import cfgv import pytest import pre_commit.constants\",\n \"merging works, if this schema has any defaults they will clobber potentially useful\",\n \"test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f =\",\n \") ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema',\",\n \"not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',))\",\n \"'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert\",\n \"x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys == set(warn_additional.keys)\",\n \"), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as\",\n \"dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a\",\n \"ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj,\",\n \"pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib\",\n \"is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]}\",\n \"' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val =\",\n \"[dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj,\",\n \"'baz'], }, ], }], }, True, ), ( { 'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks',\",\n \"f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n'\",\n \"key {'pony': '1.0'}, # not a string for version {'python': 3}, ), )\",\n \"'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name':\",\n \"{'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]}\",\n \"sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo':\",\n \"# invalid to set a language for a meta hook {'repo': 'meta', 'hooks':\",\n \"cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if this\",\n \"'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct =\",\n \"'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml\",\n \"( { 'repos': [{ 'repo': 'git<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}],\",\n \"'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A\",\n \"i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to\",\n \"A regression in 0.13.5: always_run and files are permissible [{ 'id': 'a', 'name':\",\n \"pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib\",\n \"0.13.5: always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'})\",\n \"'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(\",\n \"{'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be string\",\n \") def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language\",\n \"for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name\",\n \"def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f\",\n \"@pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( (\",\n \"[ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a string\",\n \"always_run and files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"'repos': [{ 'repo': '<EMAIL>:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$',\",\n \"is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'},\"\n]","isConversational":false}}},{"rowIdx":3485,"cells":{"ngram":{"kind":"list like","value":["KIND, either express or implied. # See the License for the specific language","Unless required by applicable law or agreed to in writing, software # distributed","for key in output_dict.keys(): if key in misc_keys: continue if key not in","output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel","= wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1,","on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self,","keys.append(key) # Main tabs index = 0 for key in keys: output =","# Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font.","output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND","not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel,","self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output","this file except in compliance with the License. # You may obtain a","tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key","<NAME> # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","* from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext","ANY KIND, either express or implied. # See the License for the specific","wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook)","buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs)","ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue","key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel,","====== # Copyright (C) 2015 <NAME> # # Licensed under the Apache License,","import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from","VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self,","= wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set","tabs index = 0 for key in keys: output = output_dict.get(key) if not","Main tabs index = 0 for key in keys: output = output_dict.get(key) if","output # Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture,","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","self.capture.panel is a part of self.frame. This Bind propagates # capture's source change","[self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind","ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue if key","self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event):","# Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc","ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture","OF ANY KIND, either express or implied. # See the License for the","ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import * _ =","# Copyright (C) 2015 <NAME> # # Licensed under the Apache License, Version","limitations under the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs","in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel,","specific language governing permissions and # limitations under the License. # import gettext","size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL,","+= 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer","in keys: keys.append(key) # Main tabs index = 0 for key in keys:","'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event):","= wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply","key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title =","self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output =","wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold","software # distributed under the License is distributed on an \"AS IS\" BASIS,","# import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import *","under the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from","UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame()","self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates #","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys","return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY)","wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT,","_init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook","button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is","def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500))","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import * _","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons.","= {} for output in outputs: output_dict[output.__class__] = output # Keys for outputs","import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui =","= None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY,","required by applicable law or agreed to in writing, software # distributed under","for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs","# self.capture.panel is a part of self.frame. This Bind propagates # capture's source","applicable law or agreed to in writing, software # distributed under the License","def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def","import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def","output_dict.keys(): if key in misc_keys: continue if key not in keys: keys.append(key) #","self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame,","or agreed to in writing, software # distributed under the License is distributed","+ self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates","[ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if","from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class","CONDITIONS OF ANY KIND, either express or implied. # See the License for","self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(),","wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not","ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab. misc_keys =","button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font =","_(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame,","wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel)","under the Apache License, Version 2.0 (the \"License\"); # you may not use","writing, software # distributed under the License is distributed on an \"AS IS\"","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","and # limitations under the License. # import gettext import wx import wx.lib.scrolledpanel","License. # You may obtain a copy of the License at # #","= wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply)","self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame","buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) #","Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk,","self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def","output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index +=","compliance with the License. # You may obtain a copy of the License","main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for","button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default'))","show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self,","python3 # -*- coding: utf-8 -*- # # IkaLog # ====== # Copyright","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","part of self.frame. This Bind propagates # capture's source change to the preview.","output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name)","preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True)","= wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use","if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook =","outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame.","# ====== # Copyright (C) 2015 <NAME> # # Licensed under the Apache","not use this file except in compliance with the License. # You may","buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for","apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button(","wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON,","self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def","top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON,","self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a","License, Version 2.0 (the \"License\"); # you may not use this file except","<reponame>fetus-hina/IkaLog #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog # ======","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name)","# Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS,","wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import *","= [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys():","# you may not use this file except in compliance with the License.","for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot,","default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys:","agreed to in writing, software # distributed under the License is distributed on","self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of","_init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output #","_(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel)","EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self):","outputs: output_dict[output.__class__] = output # Keys for outputs in the main page. keys","Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs =","(the \"License\"); # you may not use this file except in compliance with","_(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer)","def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if","title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key)","key in output_dict.keys(): if key in misc_keys: continue if key not in keys:","Keys for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON,","a part of self.frame. This Bind propagates # capture's source change to the","-*- coding: utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015","of self.frame. This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind(","not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event)","ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab. misc_keys","# Unless required by applicable law or agreed to in writing, software #","by applicable law or agreed to in writing, software # distributed under the","wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font)","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","for output in outputs: output_dict[output.__class__] = output # Keys for outputs in the","_ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame","keys: keys.append(key) # Main tabs index = 0 for key in keys: output","for key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title","file except in compliance with the License. # You may obtain a copy","self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel,","500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply'))","ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue if","License for the specific language governing permissions and # limitations under the License.","= ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame =","Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame:","font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default =","to in writing, software # distributed under the License is distributed on an","top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON,","wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25))","implied. # See the License for the specific language governing permissions and #","\"License\"); # you may not use this file except in compliance with the","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of","self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel","self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) #","continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND |","ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils","wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply =","IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed under the","output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640,","to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins(","self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if","not in keys: keys.append(key) # Main tabs index = 0 for key in","wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply)","keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined","wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output: continue","or implied. # See the License for the specific language governing permissions and","Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) #","Apache License, Version 2.0 (the \"License\"); # you may not use this file","misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","in writing, software # distributed under the License is distributed on an \"AS","page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs","def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","if key in misc_keys: continue if key not in keys: keys.append(key) # Main","if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add(","# Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame,","ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key","not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab","import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import","the Apache License, Version 2.0 (the \"License\"); # you may not use this","you may not use this file except in compliance with the License. #","apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load","in output_dict.keys(): if key in misc_keys: continue if key not in keys: keys.append(key)","ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in","if key not in keys: keys.append(key) # Main tabs index = 0 for","button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) #","self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640,","wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui","License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import","ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return","def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict =","use this file except in compliance with the License. # You may obtain","0 for key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook)","output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel(","index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360))","the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events","[ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the","# Main tabs index = 0 for key in keys: output = output_dict.get(key)","self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame,","1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer =","output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL,","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY,","class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def","for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter","utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015 <NAME> #","import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import","2.0 (the \"License\"); # you may not use this file except in compliance","self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) #","coding: utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015 <NAME>","the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ]","_(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel =","for the specific language governing permissions and # limitations under the License. #","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","# Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs","event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__]","self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates # capture's","# -*- coding: utf-8 -*- # # IkaLog # ====== # Copyright (C)","360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD)","change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin.","# # Unless required by applicable law or agreed to in writing, software","button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer","button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont()","event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture]","keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index","express or implied. # See the License for the specific language governing permissions","ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui","language governing permissions and # limitations under the License. # import gettext import","misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY,","handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] +","= self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output","either express or implied. # See the License for the specific language governing","wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer =","= wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD)","index = 0 for key in keys: output = output_dict.get(key) if not output:","of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show()","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","] for key in output_dict.keys(): if key in misc_keys: continue if key not","combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack,","= button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame,","outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output # Keys","title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index,","continue if key not in keys: keys.append(key) # Main tabs index = 0","continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel =","if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event):","] # Keys for outputs combined into the misc tab. misc_keys = [","* _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui","the License. # You may obtain a copy of the License at #","each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise()","from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui):","button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY,","# IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed under","plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def","# distributed under the License is distributed on an \"AS IS\" BASIS, #","wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event","key not in keys: keys.append(key) # Main tabs index = 0 for key","(C) 2015 <NAME> # # Licensed under the Apache License, Version 2.0 (the","#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog # ====== #","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","governing permissions and # limitations under the License. # import gettext import wx","import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import","self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel, _('Misc.'))","into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer,","ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame(","Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY,","= wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if","misc_keys: continue if key not in keys: keys.append(key) # Main tabs index =","self.frame. This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED,","= [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into","the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context',","ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object):","with the License. # You may obtain a copy of the License at","wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output:","wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel","wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in","= 0 for key in keys: output = output_dict.get(key) if not output: continue","fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame()","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","key in misc_keys: continue if key not in keys: keys.append(key) # Main tabs","misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for","import * from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI',","self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event)","event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for","# limitations under the License. # import gettext import wx import wx.lib.scrolledpanel import","for key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index,","{} for output in outputs: output_dict[output.__class__] = output # Keys for outputs in","is a part of self.frame. This Bind propagates # capture's source change to","output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 #","law or agreed to in writing, software # distributed under the License is","the License for the specific language governing permissions and # limitations under the","wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font =","* from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import *","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font","permissions and # limitations under the License. # import gettext import wx import","ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys:","button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part","-*- # # IkaLog # ====== # Copyright (C) 2015 <NAME> # #","output_dict[output.__class__] = output # Keys for outputs in the main page. keys =","OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self):","debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event)","# Keys for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV,","= wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font","button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default)","a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel'))","in compliance with the License. # You may obtain a copy of the","propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh","on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs:","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title)","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default)","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","2015 <NAME> # # Licensed under the Apache License, Version 2.0 (the \"License\");","See the License for the specific language governing permissions and # limitations under","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a","wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","= wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer","self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict","= output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font)","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)","def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def","self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook,","in misc_keys: continue if key not in keys: keys.append(key) # Main tabs index","the specific language governing permissions and # limitations under the License. # import","output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY,","Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None","tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font =","import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from","ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab.","outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi,","self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont()","Version 2.0 (the \"License\"); # you may not use this file except in","except in compliance with the License. # You may obtain a copy of","in outputs: output_dict[output.__class__] = output # Keys for outputs in the main page.","# capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI","output in outputs: output_dict[output.__class__] = output # Keys for outputs in the main","def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output","= output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","Copyright (C) 2015 <NAME> # # Licensed under the Apache License, Version 2.0","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from","self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for","= output # Keys for outputs in the main page. keys = [","gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel","buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers","= wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key","self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self,","__init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame:","in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] #","default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer)","None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"),","self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] =","# Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL)","self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply","output_dict = {} for output in outputs: output_dict[output.__class__] = output # Keys for","on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {}","= wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer =","= Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame =","title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10)","ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc","event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs):","source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each","outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ]","self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self,","output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling()","self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL)","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font","bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default","wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer)","# # IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed","top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel)","This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized)","= [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This"],"string":"[\n \"KIND, either express or implied. # See the License for the specific language\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"for key in output_dict.keys(): if key in misc_keys: continue if key not in\",\n \"output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel\",\n \"= wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1,\",\n \"on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self,\",\n \"keys.append(key) # Main tabs index = 0 for key in keys: output =\",\n \"# Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font.\",\n \"output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND\",\n \"not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel,\",\n \"self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key\",\n \"<NAME> # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"* from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook)\",\n \"buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs)\",\n \"ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue\",\n \"key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel,\",\n \"====== # Copyright (C) 2015 <NAME> # # Licensed under the Apache License,\",\n \"import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from\",\n \"VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self,\",\n \"= wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set\",\n \"tabs index = 0 for key in keys: output = output_dict.get(key) if not\",\n \"Main tabs index = 0 for key in keys: output = output_dict.get(key) if\",\n \"output # Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture,\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"self.capture.panel is a part of self.frame. This Bind propagates # capture's source change\",\n \"[self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind\",\n \"ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue if key\",\n \"self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event):\",\n \"# Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc\",\n \"ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import * _ =\",\n \"# Copyright (C) 2015 <NAME> # # Licensed under the Apache License, Version\",\n \"limitations under the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs\",\n \"in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel,\",\n \"specific language governing permissions and # limitations under the License. # import gettext\",\n \"size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL,\",\n \"+= 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer\",\n \"in keys: keys.append(key) # Main tabs index = 0 for key in keys:\",\n \"'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event):\",\n \"= wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply\",\n \"key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title =\",\n \"self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output =\",\n \"wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"# import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import *\",\n \"under the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from\",\n \"UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame()\",\n \"self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates #\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys\",\n \"return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY)\",\n \"wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT,\",\n \"_init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook\",\n \"button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is\",\n \"def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500))\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import * _\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons.\",\n \"= {} for output in outputs: output_dict[output.__class__] = output # Keys for outputs\",\n \"import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui =\",\n \"= None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY,\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs\",\n \"# self.capture.panel is a part of self.frame. This Bind propagates # capture's source\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def\",\n \"import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def\",\n \"output_dict.keys(): if key in misc_keys: continue if key not in keys: keys.append(key) #\",\n \"self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame,\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"+ self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates\",\n \"[ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if\",\n \"from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(),\",\n \"wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not\",\n \"ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab. misc_keys =\",\n \"button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font =\",\n \"_(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame,\",\n \"wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel)\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"and # limitations under the License. # import gettext import wx import wx.lib.scrolledpanel\",\n \"License. # You may obtain a copy of the License at # #\",\n \"= wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply)\",\n \"self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame\",\n \"buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) #\",\n \"Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk,\",\n \"self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def\",\n \"output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index +=\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for\",\n \"button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default'))\",\n \"show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self,\",\n \"python3 # -*- coding: utf-8 -*- # # IkaLog # ====== # Copyright\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"part of self.frame. This Bind propagates # capture's source change to the preview.\",\n \"output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name)\",\n \"preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True)\",\n \"= wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use\",\n \"if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook =\",\n \"outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame.\",\n \"# ====== # Copyright (C) 2015 <NAME> # # Licensed under the Apache\",\n \"not use this file except in compliance with the License. # You may\",\n \"buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for\",\n \"apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button(\",\n \"wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON,\",\n \"self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def\",\n \"top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON,\",\n \"self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"<reponame>fetus-hina/IkaLog #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog # ======\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name)\",\n \"# Keys for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS,\",\n \"wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import *\",\n \"= [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys():\",\n \"# you may not use this file except in compliance with the License.\",\n \"for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot,\",\n \"default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys:\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of\",\n \"_init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output #\",\n \"_(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel)\",\n \"EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self):\",\n \"outputs: output_dict[output.__class__] = output # Keys for outputs in the main page. keys\",\n \"Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs =\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"_(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer)\",\n \"def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if\",\n \"title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key)\",\n \"key in output_dict.keys(): if key in misc_keys: continue if key not in keys:\",\n \"Keys for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON,\",\n \"a part of self.frame. This Bind propagates # capture's source change to the\",\n \"-*- coding: utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015\",\n \"of self.frame. This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind(\",\n \"not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event)\",\n \"ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab. misc_keys\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font)\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"for output in outputs: output_dict[output.__class__] = output # Keys for outputs in the\",\n \"_ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame\",\n \"keys: keys.append(key) # Main tabs index = 0 for key in keys: output\",\n \"for key in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel,\",\n \"500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply'))\",\n \"ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys: continue if\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"= ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame =\",\n \"Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame:\",\n \"font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default =\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON,\",\n \"wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25))\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of\",\n \"self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel\",\n \"self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) #\",\n \"continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND |\",\n \"ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils\",\n \"wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply =\",\n \"IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed under the\",\n \"output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640,\",\n \"to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins(\",\n \"self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if\",\n \"not in keys: keys.append(key) # Main tabs index = 0 for key in\",\n \"wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply)\",\n \"keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined\",\n \"wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output: continue\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) #\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs\",\n \"def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"if key in misc_keys: continue if key not in keys: keys.append(key) # Main\",\n \"if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add(\",\n \"# Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame,\",\n \"ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key\",\n \"not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab\",\n \"import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"you may not use this file except in compliance with the License. #\",\n \"apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load\",\n \"in output_dict.keys(): if key in misc_keys: continue if key not in keys: keys.append(key)\",\n \"ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in\",\n \"if key not in keys: keys.append(key) # Main tabs index = 0 for\",\n \"button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) #\",\n \"self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640,\",\n \"wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui\",\n \"License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import\",\n \"ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return\",\n \"def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict =\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"0 for key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook)\",\n \"output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel(\",\n \"index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360))\",\n \"the License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events\",\n \"[ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the\",\n \"# Main tabs index = 0 for key in keys: output = output_dict.get(key)\",\n \"self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame,\",\n \"1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer =\",\n \"output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL,\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY,\",\n \"class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def\",\n \"for outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter\",\n \"utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015 <NAME> #\",\n \"import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) #\",\n \"coding: utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015 <NAME>\",\n \"the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ]\",\n \"_(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel =\",\n \"for the specific language governing permissions and # limitations under the License. #\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"# Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs\",\n \"event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__]\",\n \"self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind propagates # capture's\",\n \"# -*- coding: utf-8 -*- # # IkaLog # ====== # Copyright (C)\",\n \"360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD)\",\n \"change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin.\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer\",\n \"button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont()\",\n \"event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture]\",\n \"keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui\",\n \"language governing permissions and # limitations under the License. # import gettext import\",\n \"misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY,\",\n \"handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] +\",\n \"= self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output\",\n \"either express or implied. # See the License for the specific language governing\",\n \"wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer =\",\n \"= wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD)\",\n \"index = 0 for key in keys: output = output_dict.get(key) if not output:\",\n \"of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show()\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"] for key in output_dict.keys(): if key in misc_keys: continue if key not\",\n \"combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack,\",\n \"= button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame,\",\n \"outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output # Keys\",\n \"title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index,\",\n \"continue if key not in keys: keys.append(key) # Main tabs index = 0\",\n \"continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel =\",\n \"if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event):\",\n \"] # Keys for outputs combined into the misc tab. misc_keys = [\",\n \"* _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui\",\n \"the License. # You may obtain a copy of the License at #\",\n \"each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise()\",\n \"from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui):\",\n \"button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY,\",\n \"# IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed under\",\n \"plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event\",\n \"key not in keys: keys.append(key) # Main tabs index = 0 for key\",\n \"(C) 2015 <NAME> # # Licensed under the Apache License, Version 2.0 (the\",\n \"#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog # ====== #\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"governing permissions and # limitations under the License. # import gettext import wx\",\n \"import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import\",\n \"self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel, _('Misc.'))\",\n \"into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer,\",\n \"ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame(\",\n \"Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY,\",\n \"= wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if\",\n \"misc_keys: continue if key not in keys: keys.append(key) # Main tabs index =\",\n \"self.frame. This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED,\",\n \"= [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into\",\n \"the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context',\",\n \"ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object):\",\n \"with the License. # You may obtain a copy of the License at\",\n \"wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not output:\",\n \"wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel\",\n \"wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in\",\n \"= 0 for key in keys: output = output_dict.get(key) if not output: continue\",\n \"fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame()\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"key in misc_keys: continue if key not in keys: keys.append(key) # Main tabs\",\n \"misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for\",\n \"import * from ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI',\",\n \"self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event)\",\n \"event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for\",\n \"# limitations under the License. # import gettext import wx import wx.lib.scrolledpanel import\",\n \"for key in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index,\",\n \"{} for output in outputs: output_dict[output.__class__] = output # Keys for outputs in\",\n \"is a part of self.frame. This Bind propagates # capture's source change to\",\n \"output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 #\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font =\",\n \"* from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import *\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font\",\n \"permissions and # limitations under the License. # import gettext import wx import\",\n \"ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys:\",\n \"button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part\",\n \"-*- # # IkaLog # ====== # Copyright (C) 2015 <NAME> # #\",\n \"output_dict[output.__class__] = output # Keys for outputs in the main page. keys =\",\n \"OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self):\",\n \"debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event)\",\n \"# Keys for outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV,\",\n \"= wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font\",\n \"button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default)\",\n \"a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel'))\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh\",\n \"on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs:\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title)\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default)\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"2015 <NAME> # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a\",\n \"wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"= wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer\",\n \"self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict\",\n \"= output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font)\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)\",\n \"def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def\",\n \"self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook,\",\n \"in misc_keys: continue if key not in keys: keys.append(key) # Main tabs index\",\n \"the specific language governing permissions and # limitations under the License. # import\",\n \"output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY,\",\n \"Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None\",\n \"tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font =\",\n \"import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from\",\n \"ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc tab.\",\n \"outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi,\",\n \"self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont()\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"in outputs: output_dict[output.__class__] = output # Keys for outputs in the main page.\",\n \"# capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI\",\n \"output in outputs: output_dict[output.__class__] = output # Keys for outputs in the main\",\n \"def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] = output\",\n \"= output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"Copyright (C) 2015 <NAME> # # Licensed under the Apache License, Version 2.0\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture from\",\n \"self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for\",\n \"= output # Keys for outputs in the main page. keys = [\",\n \"gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel\",\n \"buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers\",\n \"= wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key\",\n \"self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self,\",\n \"__init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame:\",\n \"in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] #\",\n \"default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer)\",\n \"None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"),\",\n \"self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__] =\",\n \"# Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL)\",\n \"self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply\",\n \"output_dict = {} for output in outputs: output_dict[output.__class__] = output # Keys for\",\n \"on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {}\",\n \"= wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer =\",\n \"= Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame =\",\n \"title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10)\",\n \"ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the misc\",\n \"event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs):\",\n \"source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each\",\n \"outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ]\",\n \"self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self,\",\n \"output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling()\",\n \"self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL)\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font\",\n \"bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default\",\n \"wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer)\",\n \"# # IkaLog # ====== # Copyright (C) 2015 <NAME> # # Licensed\",\n \"top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel)\",\n \"This Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized)\",\n \"= [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This\"\n]","isConversational":false}}},{"rowIdx":3486,"cells":{"ngram":{"kind":"list like","value":["f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0',","= f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[","author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']}","find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB',","author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']} )","import setup, find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot',","open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>',","long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']),","long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx':","setup, find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0',","from setuptools import setup, find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read()","with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description,","'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>',","as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB',","description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ],","name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift',","setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests',","f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK',","version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI'","setuptools import setup, find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup("],"string":"[\n \"f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0',\",\n \"= f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[\",\n \"author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']}\",\n \"find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB',\",\n \"author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']} )\",\n \"import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot',\",\n \"open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>',\",\n \"long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']),\",\n \"long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx':\",\n \"setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0',\",\n \"from setuptools import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read()\",\n \"with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description,\",\n \"'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>',\",\n \"as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB',\",\n \"description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ],\",\n \"name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift',\",\n \"setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests',\",\n \"f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK',\",\n \"version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI'\",\n \"setuptools import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup(\"\n]","isConversational":false}}},{"rowIdx":3487,"cells":{"ngram":{"kind":"list like","value":["for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB","routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\")","configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name,","= [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item =","= {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] =","{ static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list =","route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route","()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] =","= self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"] =","route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" %","acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info","% (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired.","@decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str,","= self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] =","tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): '''","tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] =","tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\")","= ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,)) return","is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info","= {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] =","is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. '''","GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel","= tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self):","driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB","Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }]","tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return","DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration '''","%s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is","''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str,","Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver","configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class","get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route","dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {}","@decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{","get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log def","uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni =","\"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for","information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] }","acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info =","-*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation #","def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message","{} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log","''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self):","@decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self):","GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static","return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict:","subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = [] routes =","{} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] =","vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list =","is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] }","def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str,","''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB):","import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): '''","= %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information","}] } ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\",","dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list","route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info","GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE","information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } '''","get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str,","PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }]","tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe =","route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"]","{}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name =","class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): '''","acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info","tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\",","''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None,","#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and","tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information","{} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id","information is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info =","tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,))","}] } ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\",","tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item","get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management","= {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in","in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] =","tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list","__init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message =","''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info =","tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item)","# -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation","utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py","(get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired","} ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ())","= vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] =","static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = []","tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log","is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } '''","DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def","-*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py '''","= self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self):","''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str","CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None,","routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"]","tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] =","coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename:","get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info","self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\")","and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration","super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information","tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item","uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\"","get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo =","tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,))","<PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe","= self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log","pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\",","= uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if =","tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log","''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service","pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list =","= [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item =","[] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {}","vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo:","{} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\")","}] } ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\",","is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\"","CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog","= self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\")","= %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information","tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"]","= uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list","tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): '''","def get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str,","= {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"]","uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni","%s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is","tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"]","= tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"]","''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo","# Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule","management_info = %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route","acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {}","in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] =","interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str,","{ tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {}","get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str,","gateway_address:str }] } ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\",","tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] =","acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info","uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route =","%s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired.","''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for","get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: {","= ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"]","static_route = %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface","for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\")","= tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item)","ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log","ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] =","ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired.","= route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return","()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\")","information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired.","(get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired","# Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter","in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] =","tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"]","''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information","2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for","= ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\"","''' Management information is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get","python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone","get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log def","acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info =","{} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos:","Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module","decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver","module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from","= tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item","= tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self):","for route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\")","tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name =","device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"]","self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def","% (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired","''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,))","()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in","db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired. '''","''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message,","Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is","= {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in","[] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item = {}","username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = []","GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter","get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict: {","} ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ())","route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] }","} ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ())","tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"]","for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor","self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass","Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log","Management information is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info","= self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id =","EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for","tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\",","= route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\"","tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id","acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" %","self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): '''","acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } '''","tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"]","= tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"]","= <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] =","get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str,","for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\")","(get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict:","{} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes:","ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"]","tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos =","tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item)","% (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired.","tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" %","get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def","''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str","= {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get(","get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\")","import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh","Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh","return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: {","ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"]","= %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is","@decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info)","tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name","''' get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for","Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor '''","return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict:","uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {}","tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if","@decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{","from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration","ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,)) return get_info","Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import","Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None):","driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB,","= {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info","ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = [] routes","\"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info","if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = []","def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str,","Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str,","import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log","= [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ())","self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is","= route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): '''","def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): '''","db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def","= tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,))","Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from","{} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\",","<filename>lib/SeparateDriver/CgwshDeviceDriverSetParameterECDB.py #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph","@decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\"device_name\"]","self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name","''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import","dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info =","pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info = {}","{}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel","uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name","{}).get(\"staticRoute\", ()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"]","ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" %","from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class","def get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\"device_name\"] =","get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return","[] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name","tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item =","{ pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list","Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration '''","vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get(","ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\")","self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\")","get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{","tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list","Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import","self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\")","information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }]","class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self,","{}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"]","password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos","tmp_item[\"password\"] = <PASSWORD>.get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list"],"string":"[\n \"for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB\",\n \"routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\")\",\n \"configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name,\",\n \"= [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item =\",\n \"= {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] =\",\n \"{ static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list =\",\n \"route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route\",\n \"()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] =\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] =\",\n \"route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" %\",\n \"acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info\",\n \"% (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired.\",\n \"@decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str,\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] =\",\n \"tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): '''\",\n \"tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] =\",\n \"tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\")\",\n \"= ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,)) return\",\n \"is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info\",\n \"= {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] =\",\n \"is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. '''\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel\",\n \"= tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self):\",\n \"driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB\",\n \"Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }]\",\n \"tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return\",\n \"DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration '''\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is\",\n \"''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str,\",\n \"Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver\",\n \"configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class\",\n \"get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route\",\n \"dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {}\",\n \"@decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{\",\n \"get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni =\",\n \"\\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for\",\n \"information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] }\",\n \"acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info =\",\n \"-*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation #\",\n \"def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message\",\n \"{} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self):\",\n \"@decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self):\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static\",\n \"return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict:\",\n \"subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = [] routes =\",\n \"{} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] =\",\n \"vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list =\",\n \"is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] }\",\n \"def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str,\",\n \"''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB):\",\n \"import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): '''\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information\",\n \"}] } ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\",\",\n \"dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list\",\n \"route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE\",\n \"information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } '''\",\n \"get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str,\",\n \"PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }]\",\n \"tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe =\",\n \"route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"]\",\n \"{}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name =\",\n \"class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): '''\",\n \"acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info\",\n \"tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\",\",\n \"''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None,\",\n \"#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and\",\n \"tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information\",\n \"{} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id\",\n \"information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info =\",\n \"tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,))\",\n \"}] } ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\",\",\n \"tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item\",\n \"get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management\",\n \"= {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in\",\n \"in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] =\",\n \"tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list\",\n \"__init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message =\",\n \"''' pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info =\",\n \"tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item)\",\n \"# -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation\",\n \"utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py\",\n \"(get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired\",\n \"} ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ())\",\n \"= vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] =\",\n \"static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = []\",\n \"tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } '''\",\n \"DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def\",\n \"-*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py '''\",\n \"= self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self):\",\n \"''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str\",\n \"CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None,\",\n \"routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"]\",\n \"tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] =\",\n \"coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename:\",\n \"get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\")\",\n \"and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration\",\n \"super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information\",\n \"tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item\",\n \"uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\"\",\n \"get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo =\",\n \"tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,))\",\n \"<PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe\",\n \"= self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log\",\n \"pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"= uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if =\",\n \"tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service\",\n \"pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list =\",\n \"= [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item =\",\n \"[] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {}\",\n \"vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo:\",\n \"{} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\")\",\n \"}] } ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\",\",\n \"is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\"\",\n \"CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\")\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information\",\n \"tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"]\",\n \"= uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list\",\n \"tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): '''\",\n \"def get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str,\",\n \"= {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"]\",\n \"uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is\",\n \"tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"]\",\n \"= tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"]\",\n \"''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo\",\n \"# Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule\",\n \"management_info = %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route\",\n \"acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {}\",\n \"in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] =\",\n \"interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str,\",\n \"{ tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {}\",\n \"get_static_route_info(self): ''' Static route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str,\",\n \"gateway_address:str }] } ''' get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] =\",\n \"acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info\",\n \"uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route =\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired.\",\n \"''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for\",\n \"get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: {\",\n \"= ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"]\",\n \"static_route = %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface\",\n \"for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\")\",\n \"= tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item)\",\n \"ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log\",\n \"ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] =\",\n \"ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired.\",\n \"= route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return\",\n \"()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\")\",\n \"information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is acquired.\",\n \"(get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired\",\n \"# Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter\",\n \"in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] =\",\n \"tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"]\",\n \"''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information\",\n \"2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for\",\n \"= ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\"\",\n \"''' Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone\",\n \"get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info =\",\n \"{} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos:\",\n \"Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module\",\n \"decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver\",\n \"module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from\",\n \"= tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item\",\n \"= tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self):\",\n \"for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\")\",\n \"tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name =\",\n \"device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"]\",\n \"self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def\",\n \"% (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired\",\n \"''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,))\",\n \"()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in\",\n \"db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired. '''\",\n \"''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message,\",\n \"Service information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is\",\n \"= {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in\",\n \"[] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {}\",\n \"username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = []\",\n \"GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter\",\n \"get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired dict: {\",\n \"} ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ())\",\n \"route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] }\",\n \"} ''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ())\",\n \"tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"]\",\n \"for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor\",\n \"self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass\",\n \"Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log\",\n \"Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info\",\n \"= self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id =\",\n \"EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for\",\n \"tunnel_source:str, }] } ''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"= route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\"\",\n \"tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id\",\n \"acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" %\",\n \"self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): '''\",\n \"acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } '''\",\n \"tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"]\",\n \"= tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"]\",\n \"= <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] =\",\n \"get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str,\",\n \"for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\")\",\n \"(get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict:\",\n \"{} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes:\",\n \"ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"]\",\n \"tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos =\",\n \"tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item)\",\n \"% (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired.\",\n \"tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" %\",\n \"get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str\",\n \"= {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get(\",\n \"get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\")\",\n \"import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh\",\n \"Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh\",\n \"return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: {\",\n \"ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"]\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is\",\n \"@decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info)\",\n \"tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name\",\n \"''' get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for\",\n \"Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor '''\",\n \"return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict:\",\n \"uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {}\",\n \"tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if\",\n \"@decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{\",\n \"from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration\",\n \"ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,)) return get_info\",\n \"Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import\",\n \"Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None):\",\n \"driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB,\",\n \"= {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info\",\n \"ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list = [] routes\",\n \"\\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info\",\n \"if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list = []\",\n \"def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str,\",\n \"Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str,\",\n \"import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log\",\n \"= [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ())\",\n \"self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is\",\n \"= route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): '''\",\n \"def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def get_management_info(self): '''\",\n \"db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def\",\n \"= tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,))\",\n \"Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from\",\n \"{} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\",\",\n \"<filename>lib/SeparateDriver/CgwshDeviceDriverSetParameterECDB.py #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph\",\n \"@decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"]\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name\",\n \"''' Parameter module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import\",\n \"dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info =\",\n \"pass @decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info = {}\",\n \"{}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel\",\n \"uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name\",\n \"{}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"]\",\n \"ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" %\",\n \"from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class\",\n \"def get_management_info(self): ''' Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] =\",\n \"get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return\",\n \"[] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name\",\n \"tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item =\",\n \"{ pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list\",\n \"Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration '''\",\n \"vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get(\",\n \"ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\")\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\")\",\n \"get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{\",\n \"tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list\",\n \"Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import\",\n \"self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\")\",\n \"information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }]\",\n \"class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self,\",\n \"{}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"]\",\n \"password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list = [] ppp_infos\",\n \"tmp_item[\\\"password\\\"] = <PASSWORD>.get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list\"\n]","isConversational":false}}},{"rowIdx":3488,"cells":{"ngram":{"kind":"list like","value":["new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip,","for the docker registry @param pwd the password for the docker registry functional","zip_file the zip file to be extracted @param to_dir the destination directory to","unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle","for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle","used for recursive calls) @return True if the list of actual objects contain","= \"directory\" # directory may have children # the children in a directory","# if actual is a dict then verify it and its children if","follows IBM defined structure. False otherwise. \"\"\" isMatched = True if type(expected) is","structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not","Dockerfile :param str docker_url: the docker registry VIP accessible from the mesos slaves","== 'directory': currentPath = currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\":","to build paths recursively for error msg) @param isMatched (only used for recursive","def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the global list of","does not meet predefined structure. Could not proceed with deployment.\") # clean up","} # TEMP: Remove the suppressed verification once the docker cert location #","openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param jsonFile path to the","== \"yes\": # log error msg and return false if needed isMatched =","= tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting","of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile","false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return false if","else: ret = True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle):","zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory","import datetime import re import operator import unicodedata # global list of error","is not valid: \" + jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function","convert @return JSON representation of a directory \"\"\" d = {'name': os.path.basename(directory)} #","a ZIP file is good or bad @param filePath the zip file to","dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT']","True # if the contents of the directory can be anything then do","is a dict then verify it and its children if type(actual) is dict:","(only used for recursive calls) @return True if the list of actual objects","uploaded bundle follows IBM defined structure. False otherwise. \"\"\" isMatched = True if","unzipped. Could not proceed with deployment.\") # out put report value , join","bundle follows IBM defined structure @param expected the JSON representation of the IBM","location # is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url,","is a list then find out if they match anywhere, if so get","subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def","a list then find out if they match anywhere, if so get the","build paths recursively for error msg) @return True if structure of the uploaded","isMatched = isMatched & True # if the contents of the directory can","verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\"))","\"\"\" # if actual is a dict then verify it and its children","a JSON file @param jsonFile path to the JSON file @return the loaded","be the one with the highest version number (vX) and the latest timestamp","pwd the password for the docker registry functional user @return a JSON response","tag \"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list","\"\"\" Function to convert objects in a given directory into JSON form. The","unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying","name of the image :param str image_name: the name of the image :param","url of the REST call @param usr the functional username for the docker","not valid: \" + jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to","too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if","file \"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle)","= directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure,","JSON representation of the IBM defined structure @param actual the JSON representation of","no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do","match. They must have the same name and must both be the same","the global array. @param expected the expected element @param currentPath the current path","\"directory\" # directory may have children # the children in a directory is","break if matchedPosition != -1: # if they match then verify their children","to the global list of errorMessages @param errorMessage the error message to add","isMatched): \"\"\" Function to verify if an actual list of objects contains an","= dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip","be the same type. @param expectedElement the expected element. May be defined by","FROM command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace + \"/\" +","matches(expected,actual): # a matching file or directory was found if expected['type'] == 'directory':","Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM","elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag","@param filePath the zip file to be tested @return True if the ZIP","scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python","put report value , join all the messages together print (\"report=[\" + \".","True if the list of actual objects contain the expected element \"\"\" #","adds error messages to the global array. @param expected the expected element @param","vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one with the","bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo):","expectedElement the expected element. May be defined by regular expression @param actualElement the","range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected,","JSON file is not valid: \" + jsonFile) raise return data def directoryToJSON(directory):","True if type(expected) is dict: if matches(expected,actual): # a matching file or directory","docker registry URL associated with the datacenter this script is being run on","logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in range(0,len(expected)): isMatched","currentPath): \"\"\" Function to verify if an uploaded bundle follows IBM defined structure","docker registry VIP accessible from the mesos slaves :param str image_namespace: the name","that has the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is","\"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM","uploaded bundle follows IBM defined structure @param expected the JSON representation of the","import unicodedata # global list of error messages to keep track of all","A directory is composed of a list and may contain files and/or directories.","the one with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm).","script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\")","a directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for","expected element \"\"\" # if actual is a dict then verify it and","for recursive calls) @return True if the list of actual objects contain the","def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if an actual list","common functions shared by each individual build scripts. \"\"\" def get(url, usr, pwd):","@param zip_file the zip file to be extracted @param to_dir the destination directory","path we are on that has the missing file or directory \"\"\" addToErrorMessages(\"A","out if they match anywhere, if so get the matched position elif type(actual)","isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function","actual is a dict then verify it and its children if type(actual) is","list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else:","a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile","the functional username for the docker registry @param pwd the password for the","of a directory \"\"\" d = {'name': os.path.basename(directory)} # the parent object is","if the ZIP file is good. False otherwise. \"\"\" ret = True try:","+ os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile):","the latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest,","continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except","except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if","cert location # is figured out and we specify it in REQUESTS_CA_BUNDLE return","could not be unzipped. Could not proceed with deployment.\") # out put report","return data def directoryToJSON(directory): \"\"\" Function to convert objects in a given directory","expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return false if needed isMatched","for x in os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual,","then find out if they match anywhere, if so get the matched position","# global list of error messages to keep track of all error msgs","def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to the global array.","to build paths recursively for error msg) @return True if structure of the","= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp:","the expected element \"\"\" # if actual is a dict then verify it","dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError:","+ '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in","directory into JSON form. The parent object is always a dict, it may","of common functions shared by each individual build scripts. \"\"\" def get(url, usr,","if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo):","@param pwd the password for the docker registry functional user @return the latest","all error msgs errorMessages = [] \"\"\" Collection of Common Functions used by",":param str image_namespace: the name of the image :param str image_name: the name","the password for the docker registry functional user @return the latest image tag","update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in","(vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr","objects in a given directory into JSON form. The parent object is always","= [] \"\"\" Collection of Common Functions used by Build Scripts A collection","extracted @param to_dir the destination directory to extract the zip file to \"\"\"","of the image :param str image_tag_latest: the latest version tag of the base","# Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT","json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash,","matching file or directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath)","recursive calls) @return True if the list of actual objects contain the expected","os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure","actual['children'], currentPath) # do further checking else: # a matching file or directory","False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could","# a matching file or directory was found if expected['type'] == 'directory': currentPath","bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could not proceed with","type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is","of the uploaded bundle @param currentPath the path currently being checked (used to","actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and","or bad @param filePath the zip file to be tested @return True if","errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively","in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory)","Function to open a JSON file @param jsonFile path to the JSON file","unzip function for extracting zip files @param zip_file the zip file to be","and its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath)","open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to","len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if","\"\"\" Retrieve the latest version of an image based on its tags: vX-YYYYMMDD-HHmm.","the name of the image :param str image_name: the name of the image","print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") #","build paths recursively for error msg) @param isMatched (only used for recursive calls)","object is always a dict, it may contain children if type=directory. A directory","destination directory to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as","# the parent object is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory","in os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\"","!= -1: # if they match then verify their children too isMatched =","their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else :","latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip files @param","expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k","# out put report value , join all the messages together print (\"report=[\"","zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret = False else:","the docker registry VIP accessible from the mesos slaves :param str image_namespace: the","# convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\")","jsonFile path to the JSON file @return the loaded JSON file \"\"\" try:","then log the error msg and return false if expectedElement['fail-if-not-found'] == \"yes\": #","d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\" return d","# update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x","\"\"\" Function to unzip a ZIP file recursively @param zipFileName the zip file","d['type'] = \"directory\" # directory may have children # the children in a","zip files @param zip_file the zip file to be extracted @param to_dir the","= os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format","into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo)","json import argparse import zipfile import shutil import requests import datetime import re","actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure.","anything then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'],","be defined by regular expression @param actualElement the actual element \"\"\" ret =","\"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ----------------------------------------------------------------------------------","follows IBM defined structure @param expected the JSON representation of the IBM defined","type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k],","\"FROM \" + docker_url + image_namespace + \"/\" + image_name + \":\" +","of the bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood = True","may have children # the children in a directory is a list composed","\"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list =","composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type']","os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if","bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ #","docker registry @param pwd the password for the docker registry functional user @return","registry path @param usr the functional username for the docker registry @param pwd","Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open","latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic","expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \"/\" if expected['children'] ==","ZIP file is good or bad @param filePath the zip file to be","usr, pwd): \"\"\" Retrieve the latest version of an image based on its","variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client:","structure of the uploaded bundle follows IBM defined structure. False otherwise. \"\"\" isMatched","using external Python module requests @param url the url of the REST call","dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the","by Build Scripts A collection of common functions shared by each individual build","error msg) @return True if structure of the uploaded bundle follows IBM defined","def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file is good or","uploaded bundle does not meet predefined structure. Could not proceed with deployment.\") #","and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def","python3.6 import os import subprocess import json import argparse import zipfile import shutil","url the url of the REST call @param usr the functional username for","the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd =","open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM \" + docker_url","currentPath the path currently being checked (used to build paths recursively for error","bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: #","< dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir):","\"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match.","datacenter this script is being run on :param str dockerfile_parent_dir: path to the","file recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo the","None: ret = False else: ret = True except: ret = False return","usr the functional username for the docker registry @param pwd the password for","ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is","try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag","and may contain files and/or directories. @param directory directory to convert @return JSON","verification once the docker cert location # is figured out and we specify","with the datacenter this script is being run on :param str dockerfile_parent_dir: path","@param expected the expected element @param currentPath the current path we are on","the expected element @param currentPath the current path we are on that has","registry URL associated with the datacenter this script is being run on :param","isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to the global","import zipfile import shutil import requests import datetime import re import operator import","JSON file @param jsonFile path to the JSON file @return the loaded JSON","method to verifyBundleStructure. @param actual list of the actual files and directories in","on :param str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param","'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker cert location","is being run on :param str dockerfile_parent_dir: path to the parent directory for","path currently being checked (used to build paths recursively for error msg) @param",".zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile =","Function to convert objects in a given directory into JSON form. The parent","the same name and must both be the same type. @param expectedElement the","def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an uploaded bundle follows","an uploaded bundle is: 1) a valid zip file 2) follows IBM defined","+ \"/\" + image_name + \":\" + image_tag_latest # Write the FROM command","convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if","uploaded bundle could not be unzipped. Could not proceed with deployment.\") # out","to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout():","if matches(expected,actual): # a matching file or directory was found if expected['type'] ==","= \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if","except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify","& True # if the contents of the directory can be anything then","verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON file","to adds error messages to the global array. @param expected the expected element","the matched position elif type(actual) is list: matchedPosition = -1 for i in","of all error msgs errorMessages = [] \"\"\" Collection of Common Functions used","element. Helper method to verifyBundleStructure. @param actual list of the actual files and","Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout","bundle @param currentPath the path currently being checked (used to build paths recursively","test if a ZIP file is good or bad @param filePath the zip","an uploaded bundle follows IBM defined structure @param expected the JSON representation of","os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\"","to find in the bundle @param currentPath the path currently being checked (used","version tag of the base image :returns: None \"\"\" # Form the path","currentPath) return False if type(expected) is list: for k in range(0,len(expected)): isMatched =","= open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM \" +","mesos slaves :param str image_namespace: the name of the image :param str image_name:","= tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version","the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker","checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking","Scripts A collection of common functions shared by each individual build scripts. \"\"\"","expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched):","except: addToErrorMessages(\"The specified JSON file is not valid: \" + jsonFile) raise return","Collection of Common Functions used by Build Scripts A collection of common functions","matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i","response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed","match then log the error msg and return false if expectedElement['fail-if-not-found'] == \"yes\":","----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) #","addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the global list of errorMessages","= dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif latest_version","latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:","# TEMP: Remove the suppressed verification once the docker cert location # is","directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle))","or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path: \\\"\"","else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag","is located @param nameOfBundle name of the bundle ZIP file \"\"\" print ('Validating","+ \"/\" if expected['children'] == \"_any\": isMatched = isMatched & True # if","print (\"report=[\" + \". \".join(str(x) for x in errorMessages) + \"]\") return bundleIsGood","latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif","specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr,","latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp =","each individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests","command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace + \"/\" + image_name","image_tag_latest: the latest version tag of the base image :returns: None \"\"\" #",":param str docker_url: the docker registry VIP accessible from the mesos slaves :param","name of the image :param str image_tag_latest: the latest version tag of the","image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct docker registry URL associated","msgs errorMessages = [] \"\"\" Collection of Common Functions used by Build Scripts","currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check if","shutil import requests import datetime import re import operator import unicodedata # global","defined by regular expression @param actualElement the actual element \"\"\" ret = False","ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp =","= False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an","the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a","currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the","with deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could","being checked (used to build paths recursively for error msg) @param isMatched (only","VIP accessible from the mesos slaves :param str image_namespace: the name of the","# Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the","global list of errorMessages @param errorMessage the error message to add \"\"\" print(errorMessage)","file is good. False otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath)","return def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file is good","\"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python module","by each individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET","addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path: \\\"\" + currentPath +","a dockerfile using the correct docker registry URL associated with the datacenter this","& verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match then","return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage):","return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the","\"\"\" isMatched = True if type(expected) is dict: if matches(expected,actual): # a matching","is defined to be the one with the highest version number (vX) and","for the Dockerfile :param str docker_url: the docker registry VIP accessible from the","def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded bundle is: 1)","the image :param str image_name: the name of the image :param str image_tag_latest:","message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\"","Could not proceed with deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The","\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure =============================================== def","the directory can be anything then do no further checking else: isMatched =","import shutil import requests import datetime import re import operator import unicodedata #","together print (\"report=[\" + \". \".join(str(x) for x in errorMessages) + \"]\") return","zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the","was found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \"/\"","\"\"\" Collection of Common Functions used by Build Scripts A collection of common","the password for the docker registry functional user @return a JSON response \"\"\"","\"\"\" Function to check if files/directories match. They must have the same name","where the bundle ZIP is located @param nameOfBundle name of the bundle ZIP","a matching file or directory was found if expected['type'] == 'directory': currentPath =","has the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing","+ docker_url + image_namespace + \"/\" + image_name + \":\" + image_tag_latest #","ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if an actual","subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to","directories in the bundle @param expectedElement the expected element to find in the","tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format)","expected['children'] == \"_any\": isMatched = isMatched & True # if the contents of","a dict then verify it and its children if type(actual) is dict: isMatched","bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working","data def directoryToJSON(directory): \"\"\" Function to convert objects in a given directory into","= False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be unzipped.","= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version:","\"\"\" Function to test if a ZIP file is good or bad @param","the base image :returns: None \"\"\" # Form the path for the Dockerfile","error msg) @param isMatched (only used for recursive calls) @return True if the","unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood:","#check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory =","in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1:","loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except:","otherwise. \"\"\" isMatched = True if type(expected) is dict: if matches(expected,actual): # a","verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined","False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not proceed with deployment.\")","= os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test","# directory may have children # the children in a directory is a","= openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON","the JSON file @return the loaded JSON file \"\"\" try: with open(jsonFile) as","structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure","messages to the global array. @param expected the expected element @param currentPath the","dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM \"","checking else: # a matching file or directory was not found if expected['fail-if-not-found']","then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath)","'directory': currentPath = currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched","# if they match then verify their children too isMatched = isMatched &","registry @param pwd the password for the docker registry functional user @return a","error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo):","the docker registry functional user @return a JSON response \"\"\" headers = {","None and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement,","actual list of the actual files and directories in the bundle @param expectedElement","have children # the children in a directory is a list composed of","import os import subprocess import json import argparse import zipfile import shutil import","destination directory to extract the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"):","extracted @param directoryToUnzipTo the destination directory to extract the zip file to \"\"\"","False if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched &","NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version","\"_any\": isMatched = isMatched & True # if the contents of the directory","directory was found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] +","bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be","error msg and return false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg","more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\"","except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp","is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a","tag of the base image :returns: None \"\"\" # Form the path for","requests using external Python module requests @param url the url of the REST","the latest version tag of the base image :returns: None \"\"\" # Form","a matching file or directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected,","dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may have children # the","to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded","IBM defined structure @param actual the JSON representation of the actual structure of",") unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP","if they match anywhere, if so get the matched position elif type(actual) is","and return false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return","defined structure. False otherwise. \"\"\" isMatched = True if type(expected) is dict: if","Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd","pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of","the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None","to be the one with the highest version number (vX) and the latest","expected['type'] +\" is missing from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails'])","matchedPosition = i break if matchedPosition != -1: # if they match then","children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if","currently being checked (used to build paths recursively for error msg) @param isMatched","------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed with","addToErrorMessages(\"The specified JSON file is not valid: \" + jsonFile) raise return data","@return True if the list of actual objects contain the expected element \"\"\"","(used to build paths recursively for error msg) @return True if structure of","tag else: if latest_version > str_version: continue elif latest_version < str_version: latest_version =","datetime import re import operator import unicodedata # global list of error messages","valid: \" + jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to convert","def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively @param zipFileName","timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username for","'/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']:","same name and must both be the same type. @param expectedElement the expected","the messages together print (\"report=[\" + \". \".join(str(x) for x in errorMessages) +","of actual objects contain the expected element \"\"\" # if actual is a","\"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The","the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\"","\"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in range(0,len(expected)):","The latest, by definition, is defined to be the one with the highest","verify if an uploaded bundle is: 1) a valid zip file 2) follows","print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a","to check if files/directories match. They must have the same name and must","\":\" + image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd)","import operator import unicodedata # global list of error messages to keep track","return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an uploaded","docker cert location # is figured out and we specify it in REQUESTS_CA_BUNDLE","# if they don't match then log the error msg and return false","in a given directory into JSON form. The parent object is always a","name of the bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood =","be tested @return True if the ZIP file is good. False otherwise. \"\"\"","further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further","bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not","= isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\"","of a list and may contain files and/or directories. @param directory directory to","\"\"\" # Form the path for the Dockerfile based on the parent of","argparse import zipfile import shutil import requests import datetime import re import operator","convert objects in a given directory into JSON form. The parent object is","a given directory into JSON form. The parent object is always a dict,","the path for the Dockerfile based on the parent of the caller script","structure. False otherwise. \"\"\" isMatched = True if type(expected) is dict: if matches(expected,actual):","REST call @param usr the functional username for the docker registry @param pwd","a dict, it may contain children if type=directory. A directory is composed of","image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close","further checking else: # a matching file or directory was not found if","ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded bundle is:","actual[matchedPosition] , currentPath) else : # if they don't match then log the","calls) @return True if the list of actual objects contain the expected element","function for extracting zip files @param zip_file the zip file to be extracted","latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username","os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function","os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle","in the bundle @param currentPath the path currently being checked (used to build","else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else:","usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in","element \"\"\" # if actual is a dict then verify it and its","of errorMessages @param errorMessage the error message to add \"\"\" print(errorMessage) global errorMessges","the same type. @param expectedElement the expected element. May be defined by regular","\"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates","a ZIP file recursively @param zipFileName the zip file to be extracted @param","\"/\" if expected['children'] == \"_any\": isMatched = isMatched & True # if the","= get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-'","files and directories in the bundle @param expectedElement the expected element to find","matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if they match","# ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a","ZIP is located @param nameOfBundle name of the bundle ZIP file \"\"\" print","return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively @param","str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp","representation of the actual structure of the uploaded bundle @param currentPath the path","x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if a","ret = False else: ret = True except: ret = False return ret","= False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret","registry functional user @return a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json',","zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file is good or bad","the mesos slaves :param str image_namespace: the name of the image :param str","in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\"","= json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue str_version,","= { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the","is always a dict, it may contain children if type=directory. A directory is","isMatched & True # if the contents of the directory can be anything","directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip","expression @param actualElement the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name'])","dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function","# unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of","to be extracted @param to_dir the destination directory to extract the zip file","do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) #","get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python module requests @param","matchedPosition != -1: # if they match then verify their children too isMatched","to the global array. @param expected the expected element @param currentPath the current","os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify","is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may have children #","is not None and expectedElement['type'] == actualElement['type']: ret = True return ret def","the name of the image :param str image_tag_latest: the latest version tag of","Retrieve the latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The","stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory","os.path.isdir(directory): d['type'] = \"directory\" # directory may have children # the children in","module requests @param url the url of the REST call @param usr the","@return JSON representation of a directory \"\"\" d = {'name': os.path.basename(directory)} # the","pwd): \"\"\" HTTP/HTTPS GET requests using external Python module requests @param url the","of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is","the destination directory to extract the zip file to \"\"\" # update if","str image_namespace: the name of the image :param str image_name: the name of","keep track of all error msgs errorMessages = [] \"\"\" Collection of Common","actual list of objects contains an expected element. Helper method to verifyBundleStructure. @param","return false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return false","verify it and its children if type(actual) is dict: isMatched = isMatched &","msg) @return True if structure of the uploaded bundle follows IBM defined structure.","\" + jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to convert objects","the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def","JSON representation of a directory \"\"\" d = {'name': os.path.basename(directory)} # the parent","@param currentPath the path currently being checked (used to build paths recursively for","list of error messages to keep track of all error msgs errorMessages =","requests import datetime import re import operator import unicodedata # global list of","figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers,","accessible from the mesos slaves :param str image_namespace: the name of the image","don't match then log the error msg and return false if expectedElement['fail-if-not-found'] ==","uploaded bundle @param currentPath the path currently being checked (used to build paths","return False if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched","JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle","@param actual list of the actual files and directories in the bundle @param","image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct docker registry URL","\"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could","add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to","os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\"","\"\"\" HTTP/HTTPS GET requests using external Python module requests @param url the url","= os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the","get the matched position elif type(actual) is list: matchedPosition = -1 for i","\" seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to","all the messages together print (\"report=[\" + \". \".join(str(x) for x in errorMessages)","verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else","1) a valid zip file 2) follows IBM defined structure @param zipDirectory where","= tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag","ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']:","str image_tag_latest: the latest version tag of the base image :returns: None \"\"\"","\"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip","file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300","zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\"","try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except","image :param str image_name: the name of the image :param str image_tag_latest: the","zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a .zip","file \"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified","must have the same name and must both be the same type. @param","the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\"","@return a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP:","with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path","@return True if structure of the uploaded bundle follows IBM defined structure. False","datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version =","it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0])","was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected)","\"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if","True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None:","# if the contents of the directory can be anything then do no","nameOfBundle): \"\"\" Function to verify if an uploaded bundle is: 1) a valid","= json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid: \" + jsonFile)","import requests import datetime import re import operator import unicodedata # global list","for the Dockerfile based on the parent of the caller script dockerfile_path =","isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then","list of errorMessages @param errorMessage the error message to add \"\"\" print(errorMessage) global","currently being checked (used to build paths recursively for error msg) @return True","on that has the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\"","it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd):","os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x","is: 1) a valid zip file 2) follows IBM defined structure @param zipDirectory","the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set","given directory into JSON form. The parent object is always a dict, it","currentPath) # do further checking else: # a matching file or directory was","follows IBM defined structure @param zipDirectory where the bundle ZIP is located @param","they match anywhere, if so get the matched position elif type(actual) is list:","False else: ret = True except: ret = False return ret def verifyZipFile(zipDirectory,","\"\"\" Generic unzip function for extracting zip files @param zip_file the zip file","= verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet","directory for the Dockerfile :param str docker_url: the docker registry VIP accessible from","directory to extract the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check","isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add","in the bundle @param expectedElement the expected element to find in the bundle","to convert objects in a given directory into JSON form. The parent object","actual files and directories in the bundle @param expectedElement the expected element to","\"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood","Creates a dockerfile using the correct docker registry URL associated with the datacenter","True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to","structure @param expected the JSON representation of the IBM defined structure @param actual","an expected element. Helper method to verifyBundleStructure. @param actual list of the actual","objects contains an expected element. Helper method to verifyBundleStructure. @param actual list of","def directoryToJSON(directory): \"\"\" Function to convert objects in a given directory into JSON","filePath the zip file to be tested @return True if the ZIP file","ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to","of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure =","shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\")","currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched = isMatched &","as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid:","found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list:","# Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command","def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct","JSON form. The parent object is always a dict, it may contain children","@param directoryToUnzipTo the destination directory to extract the zip file to \"\"\" #","zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using","\"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON","tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\"","zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir,","element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type']","True if structure of the uploaded bundle follows IBM defined structure. False otherwise.","\"\"\" Creates a dockerfile using the correct docker registry URL associated with the","i break if matchedPosition != -1: # if they match then verify their","badFile is not None: ret = False else: ret = True except: ret","Function to add error messages to the global list of errorMessages @param errorMessage","of the uploaded bundle follows IBM defined structure. False otherwise. \"\"\" isMatched =","with deployment.\") # clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo)","None \"\"\" # Form the path for the Dockerfile based on the parent","to verify if an uploaded bundle follows IBM defined structure @param expected the","# do further checking else: # a matching file or directory was not","# if actual is a list then find out if they match anywhere,","if actual is a list then find out if they match anywhere, if","from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement):","and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def","directory may have children # the children in a directory is a list","the bundle ZIP is located @param nameOfBundle name of the bundle ZIP file","is missing from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def","@param actual the JSON representation of the actual structure of the uploaded bundle","---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check","to be tested @return True if the ZIP file is good. False otherwise.","actual structure of the uploaded bundle @param currentPath the path currently being checked","file @return the loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file: data","if latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp","\"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name,","headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once","(used to build paths recursively for error msg) @param isMatched (only used for","the docker registry functional user @return the latest image tag \"\"\" tag_list_url =","to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function","check if files/directories match. They must have the same name and must both","isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to","expectedElement the expected element to find in the bundle @param currentPath the path","if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try:","it may contain children if type=directory. A directory is composed of a list","directories. @param directory directory to convert @return JSON representation of a directory \"\"\"","if type=directory. A directory is composed of a list and may contain files","the datacenter this script is being run on :param str dockerfile_parent_dir: path to","= currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched = isMatched","composed of a list and may contain files and/or directories. @param directory directory","@param expected the JSON representation of the IBM defined structure @param actual the","verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if an actual list of","expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error","latest, by definition, is defined to be the one with the highest version","= registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for","children # the children in a directory is a list composed of more",":param str image_name: the name of the image :param str image_tag_latest: the latest","directory \"\"\" d = {'name': os.path.basename(directory)} # the parent object is dict if","and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath,","run on :param str dockerfile_parent_dir: path to the parent directory for the Dockerfile","return def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match. They must","import argparse import zipfile import shutil import requests import datetime import re import","the image :param str image_tag_latest: the latest version tag of the base image","this script is being run on :param str dockerfile_parent_dir: path to the parent","to convert @return JSON representation of a directory \"\"\" d = {'name': os.path.basename(directory)}","-1: # if they match then verify their children too isMatched = isMatched","= True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify","= -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break","HTTP/HTTPS GET requests using external Python module requests @param url the url of","file or directory was found if expected['type'] == 'directory': currentPath = currentPath +","latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by","a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)]","\"yes\": # log error msg and return false if needed isMatched = False","if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for","to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client: \"","directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path: \\\"\" +","@param pwd the password for the docker registry functional user @return a JSON","element. May be defined by regular expression @param actualElement the actual element \"\"\"","(YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username for the","is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in","meet predefined structure. Could not proceed with deployment.\") # clean up unzipped stuff","\"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an","password for the docker registry functional user @return a JSON response \"\"\" headers","build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external","msg and return false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and","image_namespace + \"/\" + image_name + \":\" + image_tag_latest # Write the FROM","requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest","isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: #","@param errorMessage the error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return","& verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to","Form the path for the Dockerfile based on the parent of the caller","def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python module requests","isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they","msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched","is not None: ret = False else: ret = True except: ret =","errorMessages @param errorMessage the error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage])","form. The parent object is always a dict, it may contain children if","the current path we are on that has the missing file or directory","import re import operator import unicodedata # global list of error messages to","import subprocess import json import argparse import zipfile import shutil import requests import","+ \":\" + image_tag_latest # Write the FROM command string to the Dockerfile","\"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile","file to be extracted @param to_dir the destination directory to extract the zip","\"+ expected['type'] +\" is missing from the path: \\\"\" + currentPath + \"\\\"\")","======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON","IBM defined structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle","currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages","to test if a ZIP file is good or bad @param filePath the","if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo,","dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list","@param jsonFile path to the JSON file @return the loaded JSON file \"\"\"","= os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath):","report value , join all the messages together print (\"report=[\" + \". \".join(str(x)","nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture","occurred while verifying bundle structure. Could not proceed with deployment.\") bundleIsGood = False","the url of the REST call @param usr the functional username for the","to_dir): \"\"\" Generic unzip function for extracting zip files @param zip_file the zip","both be the same type. @param expectedElement the expected element. May be defined","dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str docker_url: the","expected element to find in the bundle @param currentPath the path currently being","missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the","return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to the","not proceed with deployment.\") # out put report value , join all the","error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return","they match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition]","call @param usr the functional username for the docker registry @param pwd the","re import operator import unicodedata # global list of error messages to keep","image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to","@param to_dir the destination directory to extract the zip file to \"\"\" with","type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]):","to the parent directory for the Dockerfile :param str docker_url: the docker registry","if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual","latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file,","set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify","False otherwise. \"\"\" isMatched = True if type(expected) is dict: if matches(expected,actual): #","with deployment.\") # out put report value , join all the messages together","@return the latest image tag \"\"\" tag_list_url = registry_path + '/tags/list' request =","individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using","we are on that has the missing file or directory \"\"\" addToErrorMessages(\"A \"+","msg) @param isMatched (only used for recursive calls) @return True if the list","client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure ===============================================","directory to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref:","functional user @return a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', }","array. @param expected the expected element @param currentPath the current path we are","str image_name: the name of the image :param str image_tag_latest: the latest version","DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for","+ actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched = isMatched & True","if a ZIP file is good or bad @param filePath the zip file","path currently being checked (used to build paths recursively for error msg) @return","= False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not proceed with","children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : #","if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True","Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param jsonFile","nameOfBundle name of the bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood","if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if they","= isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't","unzip a ZIP file recursively @param zipFileName the zip file to be extracted","zip file to be extracted @param directoryToUnzipTo the destination directory to extract the","being checked (used to build paths recursively for error msg) @return True if","parent object is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may have","for tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp","contain the expected element \"\"\" # if actual is a dict then verify","directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x","dict: if matches(expected,actual): # a matching file or directory was found if expected['type']","+ currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check","file or directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return","functions shared by each individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\"","REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve","the zip file to be tested @return True if the ZIP file is","get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not","messages together print (\"report=[\" + \". \".join(str(x) for x in errorMessages) + \"]\")","= \"FROM \" + docker_url + image_namespace + \"/\" + image_name + \":\"","else: # a matching file or directory was not found if expected['fail-if-not-found'] ==","parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile","def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match. They must have","of error messages to keep track of all error msgs errorMessages = []","expected element. Helper method to verifyBundleStructure. @param actual list of the actual files","os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function","os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory -----------------------------------------------------------","object is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may have children","k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched","bad @param filePath the zip file to be tested @return True if the","#!/usr/bin/env python3.6 import os import subprocess import json import argparse import zipfile import","tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text)","pwd the password for the docker registry functional user @return the latest image","os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] = \"directory\" #","@param zipFileName the zip file to be extracted @param directoryToUnzipTo the destination directory","bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle","to keep track of all error msgs errorMessages = [] \"\"\" Collection of","the expected element to find in the bundle @param currentPath the path currently","tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return","zip file to be extracted @param to_dir the destination directory to extract the","join all the messages together print (\"report=[\" + \". \".join(str(x) for x in","out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False)","clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred","json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid: \" + jsonFile) raise","defined structure @param actual the JSON representation of the actual structure of the","if an uploaded bundle is: 1) a valid zip file 2) follows IBM","latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp =","directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure,","be extracted @param directoryToUnzipTo the destination directory to extract the zip file to","= True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not","-1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if","= tag else: if latest_version > str_version: continue elif latest_version < str_version: latest_version","script is being run on :param str dockerfile_parent_dir: path to the parent directory","= '300' print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \"","return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip files","unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively @param zipFileName the","to verify if an actual list of objects contains an expected element. Helper","the children in a directory is a list composed of more files/directories d['children']","not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could not proceed","zip file to be tested @return True if the ZIP file is good.","jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to convert objects in a","= zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret = False","directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The","is dict: if matches(expected,actual): # a matching file or directory was found if","of the base image :returns: None \"\"\" # Form the path for the","== \"_any\": isMatched = isMatched & True # if the contents of the","being run on :param str dockerfile_parent_dir: path to the parent directory for the","find out if they match anywhere, if so get the matched position elif","docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct docker registry","located @param nameOfBundle name of the bundle ZIP file \"\"\" print ('Validating bundle","the uploaded bundle @param currentPath the path currently being checked (used to build","errorMessages = [] \"\"\" Collection of Common Functions used by Build Scripts A","then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath)","actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function","version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path","if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \"/\" if expected['children']","dockerfile using the correct docker registry URL associated with the datacenter this script","docker registry functional user @return a JSON response \"\"\" headers = { 'Accept':","if os.path.isdir(directory): d['type'] = \"directory\" # directory may have children # the children","latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =","= dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp","+ jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to convert objects in","with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is","= os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory","re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True return","if structure of the uploaded bundle follows IBM defined structure. False otherwise. \"\"\"","errorMessage the error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def","os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] +","isMatched = True if type(expected) is dict: if matches(expected,actual): # a matching file","# clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception","and/or directories. @param directory directory to convert @return JSON representation of a directory","a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove","to verifyBundleStructure. @param actual list of the actual files and directories in the","path to the parent directory for the Dockerfile :param str docker_url: the docker","deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not","\"\"\" Function to adds error messages to the global array. @param expected the","= [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\" return d def","JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the","extracting zip files @param zip_file the zip file to be extracted @param to_dir","and directories in the bundle @param expectedElement the expected element to find in","are on that has the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type']","objects contain the expected element \"\"\" # if actual is a dict then","@return True if the ZIP file is good. False otherwise. \"\"\" ret =","shared by each individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS","docker_url + image_namespace + \"/\" + image_name + \":\" + image_tag_latest # Write","the JSON representation of the actual structure of the uploaded bundle @param currentPath","of the actual structure of the uploaded bundle @param currentPath the path currently","\"\"\" d = {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory):","of objects contains an expected element. Helper method to verifyBundleStructure. @param actual list","an actual list of objects contains an expected element. Helper method to verifyBundleStructure.","the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not","list of the actual files and directories in the bundle @param expectedElement the","by regular expression @param actualElement the actual element \"\"\" ret = False if","the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable","deployment.\") # clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except:","latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version >","log error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath)","image tag \"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd)","if badFile is not None: ret = False else: ret = True except:","if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could not","file to be extracted @param directoryToUnzipTo the destination directory to extract the zip","if they match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement,","the suppressed verification once the docker cert location # is figured out and","currentPath, isMatched): \"\"\" Function to verify if an actual list of objects contains","the bundle @param currentPath the path currently being checked (used to build paths","actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood =","the Dockerfile :param str docker_url: the docker registry VIP accessible from the mesos","\"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT']","= False else: ret = True except: ret = False return ret def","so get the matched position elif type(actual) is list: matchedPosition = -1 for","try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\")","structure. Could not proceed with deployment.\") bundleIsGood = False else: bundleIsGood = False","files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\" return","------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) #","correct docker registry URL associated with the datacenter this script is being run","if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual,","predefined structure. Could not proceed with deployment.\") # clean up unzipped stuff and","the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from","value , join all the messages together print (\"report=[\" + \". \".join(str(x) for","the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the",":param str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str","pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag:","not None: ret = False else: ret = True except: ret = False","in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-')","unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo,","we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path,","proceed with deployment.\") # out put report value , join all the messages","for the docker registry functional user @return the latest image tag \"\"\" tag_list_url","not be unzipped. Could not proceed with deployment.\") # out put report value","dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\"","Generic unzip function for extracting zip files @param zip_file the zip file to","to open a JSON file @param jsonFile path to the JSON file @return","file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does","actual is a list then find out if they match anywhere, if so","Function to check if files/directories match. They must have the same name and","x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile,","Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed","find in the bundle @param currentPath the path currently being checked (used to","addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\") bundleIsGood =","used by Build Scripts A collection of common functions shared by each individual","not meet predefined structure. Could not proceed with deployment.\") # clean up unzipped","have the same name and must both be the same type. @param expectedElement","and must both be the same type. @param expectedElement the expected element. May","the global list of errorMessages @param errorMessage the error message to add \"\"\"","latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for","collection of common functions shared by each individual build scripts. \"\"\" def get(url,","JSON file @return the loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file:","timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # =======================","not proceed with deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded","global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP","directory to convert @return JSON representation of a directory \"\"\" d = {'name':","an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined","for extracting zip files @param zip_file the zip file to be extracted @param","if an uploaded bundle follows IBM defined structure @param expected the JSON representation","return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the global","associated with the datacenter this script is being run on :param str dockerfile_parent_dir:","[directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected,","not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle),","be anything then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'],","docker registry functional user @return the latest image tag \"\"\" tag_list_url = registry_path","with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest):","subprocess import json import argparse import zipfile import shutil import requests import datetime","image_tag_latest): \"\"\" Creates a dockerfile using the correct docker registry URL associated with","on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the","as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a","contain files and/or directories. @param directory directory to convert @return JSON representation of","registry functional user @return the latest image tag \"\"\" tag_list_url = registry_path +","recursively for error msg) @param isMatched (only used for recursive calls) @return True","image_namespace: the name of the image :param str image_name: the name of the","continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp","bundle ZIP is located @param nameOfBundle name of the bundle ZIP file \"\"\"","the_zip_file.testzip() if badFile is not None: ret = False else: ret = True","zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory,","verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an uploaded bundle follows IBM","str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError:","structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle name of","# a matching file or directory was not found if expected['fail-if-not-found'] == \"yes\":","the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional","path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function","proceed with deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle","300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client: \" +","then verify it and its children if type(actual) is dict: isMatched = isMatched","elif type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)): if","verifying bundle structure. Could not proceed with deployment.\") bundleIsGood = False else: bundleIsGood","isMatched (only used for recursive calls) @return True if the list of actual","extract the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's","based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be","zipFileName the zip file to be extracted @param directoryToUnzipTo the destination directory to","log the error msg and return false if expectedElement['fail-if-not-found'] == \"yes\": # log","str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp","= True if type(expected) is dict: if matches(expected,actual): # a matching file or","badFile = the_zip_file.testzip() if badFile is not None: ret = False else: ret","image :returns: None \"\"\" # Form the path for the Dockerfile based on","tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp =","Common Functions used by Build Scripts A collection of common functions shared by","registry @param pwd the password for the docker registry functional user @return the","docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure","False otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip()","the correct docker registry URL associated with the datacenter this script is being","unicodedata # global list of error messages to keep track of all error","@return the loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file: data =","representation of a directory \"\"\" d = {'name': os.path.basename(directory)} # the parent object","isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match","do further checking else: # a matching file or directory was not found","element to find in the bundle @param currentPath the path currently being checked","file to be tested @return True if the ZIP file is good. False","currentPath) else : # if they don't match then log the error msg","global array. @param expected the expected element @param currentPath the current path we","and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure.","@param zipDirectory where the bundle ZIP is located @param nameOfBundle name of the","package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped","be unzipped. Could not proceed with deployment.\") # out put report value ,","be extracted @param to_dir the destination directory to extract the zip file to","ret = True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\"","& verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out if","isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out","# the children in a directory is a list composed of more files/directories","error messages to the global list of errorMessages @param errorMessage the error message","to add error messages to the global list of errorMessages @param errorMessage the","FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance","expectedElement, currentPath, isMatched): \"\"\" Function to verify if an actual list of objects","representation of the IBM defined structure @param actual the JSON representation of the","verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching file or","= i break if matchedPosition != -1: # if they match then verify","subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file is","docker_url: the docker registry VIP accessible from the mesos slaves :param str image_namespace:","package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could","== actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\"","environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker","\"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for","verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out if they","headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of an","= True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into","tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip","verifyBundleStructure. @param actual list of the actual files and directories in the bundle","True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if","definition, is defined to be the one with the highest version number (vX)","logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to the global array. @param","file is not valid: \" + jsonFile) raise return data def directoryToJSON(directory): \"\"\"","match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] ,","suppressed verification once the docker cert location # is figured out and we","the latest image tag \"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url,","to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace,","d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an uploaded bundle","uploaded bundle is: 1) a valid zip file 2) follows IBM defined structure","dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") #","latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag","open a JSON file @param jsonFile path to the JSON file @return the",":param str image_tag_latest: the latest version tag of the base image :returns: None","unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package","dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace + \"/\" + image_name +","the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------","latest_tag = tag else: if latest_version > str_version: continue elif latest_version < str_version:","if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function","to verify if an uploaded bundle is: 1) a valid zip file 2)","actual the JSON representation of the actual structure of the uploaded bundle @param","current path we are on that has the missing file or directory \"\"\"","IBM defined structure @param expected the JSON representation of the IBM defined structure","currentPath = currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched =","= isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find","addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not proceed with deployment.\") #","matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match. They must have the","command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close()","up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while","continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =","by definition, is defined to be the one with the highest version number","& verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching file","ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if","\"/\" + image_name + \":\" + image_tag_latest # Write the FROM command string","Function to test if a ZIP file is good or bad @param filePath","def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] =","bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip =","list then find out if they match anywhere, if so get the matched","contents of the directory can be anything then do no further checking else:","checked (used to build paths recursively for error msg) @return True if structure","if the list of actual objects contain the expected element \"\"\" # if","file 2) follows IBM defined structure @param zipDirectory where the bundle ZIP is","shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) #","may contain files and/or directories. @param directory directory to convert @return JSON representation","Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets","the parent object is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may","one with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param","str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str docker_url:","files and/or directories. @param directory directory to convert @return JSON representation of a","A collection of common functions shared by each individual build scripts. \"\"\" def","in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp,","a valid zip file 2) follows IBM defined structure @param zipDirectory where the","dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag","request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if","directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if","while verifying bundle structure. Could not proceed with deployment.\") bundleIsGood = False else:","latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:","zipDirectory where the bundle ZIP is located @param nameOfBundle name of the bundle","verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of an image","zip file 2) follows IBM defined structure @param zipDirectory where the bundle ZIP","@param actualElement the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is","Dockerfile based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\")","Remove the suppressed verification once the docker cert location # is figured out","anywhere, if so get the matched position elif type(actual) is list: matchedPosition =","false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\"","path to the JSON file @return the loaded JSON file \"\"\" try: with","the zip file to be extracted @param to_dir the destination directory to extract","if zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo =","of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] =","number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param","2) follows IBM defined structure @param zipDirectory where the bundle ZIP is located","+ \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories","path for the Dockerfile based on the parent of the caller script dockerfile_path","to be extracted @param directoryToUnzipTo the destination directory to extract the zip file","directoryToUnzipTo the destination directory to extract the zip file to \"\"\" # update","not proceed with deployment.\") # clean up unzipped stuff and package structure Json","operator import unicodedata # global list of error messages to keep track of","latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag =","recursively for error msg) @return True if structure of the uploaded bundle follows","the bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip","zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile","Functions used by Build Scripts A collection of common functions shared by each","Function to verify if an actual list of objects contains an expected element.","str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue","json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid: \"","error messages to the global array. @param expected the expected element @param currentPath","return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if an","+ image_name + \":\" + image_tag_latest # Write the FROM command string to","\"\"\" Function to verify if an actual list of objects contains an expected","list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition =","the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret =","expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to","verify if an uploaded bundle follows IBM defined structure @param expected the JSON","\"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification","contains an expected element. Helper method to verifyBundleStructure. @param actual list of the","to extract the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if","expected the JSON representation of the IBM defined structure @param actual the JSON","always a dict, it may contain children if type=directory. A directory is composed","the JSON representation of the IBM defined structure @param actual the JSON representation","the IBM defined structure @param actual the JSON representation of the actual structure","stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle","{'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] = \"directory\"","\\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to","not None and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual,","=============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param jsonFile path","range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: #","create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct docker","= the_zip_file.testzip() if badFile is not None: ret = False else: ret =","specified JSON file is not valid: \" + jsonFile) raise return data def","if an actual list of objects contains an expected element. Helper method to","same type. @param expectedElement the expected element. May be defined by regular expression","base image :returns: None \"\"\" # Form the path for the Dockerfile based","True if the ZIP file is good. False otherwise. \"\"\" ret = True","== \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in","path @param usr the functional username for the docker registry @param pwd the","is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath,","to_dir the destination directory to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file,","docker registry path @param usr the functional username for the docker registry @param","errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file","Python module requests @param url the url of the REST call @param usr","the docker registry @param pwd the password for the docker registry functional user","once the docker cert location # is figured out and we specify it","os import subprocess import json import argparse import zipfile import shutil import requests","from the mesos slaves :param str image_namespace: the name of the image :param","Function to verify if an uploaded bundle follows IBM defined structure @param expected","@param currentPath the current path we are on that has the missing file","it and its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual,","TEMP: Remove the suppressed verification once the docker cert location # is figured","of the directory can be anything then do no further checking else: isMatched","\"\"\" Function to open a JSON file @param jsonFile path to the JSON","error msgs errorMessages = [] \"\"\" Collection of Common Functions used by Build","tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one with","element @param currentPath the current path we are on that has the missing","else: if latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version","isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching","the error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName,","pwd): \"\"\" Retrieve the latest version of an image based on its tags:","try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file","paths recursively for error msg) @param isMatched (only used for recursive calls) @return","matching file or directory was found if expected['type'] == 'directory': currentPath = currentPath","tested @return True if the ZIP file is good. False otherwise. \"\"\" ret","else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to","bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not proceed","expected the expected element @param currentPath the current path we are on that","latest version tag of the base image :returns: None \"\"\" # Form the","expected element. May be defined by regular expression @param actualElement the actual element","@param url the url of the REST call @param usr the functional username","missing from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement,","file is good or bad @param filePath the zip file to be tested","regular expression @param actualElement the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'],","match anywhere, if so get the matched position elif type(actual) is list: matchedPosition","\"\"\" Function to verify if an uploaded bundle follows IBM defined structure @param","bundle structure. Could not proceed with deployment.\") bundleIsGood = False else: bundleIsGood =","verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds","= True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function","set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300'","actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched = isMatched & True #","image_name: the name of the image :param str image_tag_latest: the latest version tag","file @param jsonFile path to the JSON file @return the loaded JSON file","the zip file to be extracted @param directoryToUnzipTo the destination directory to extract","structure of the uploaded bundle @param currentPath the path currently being checked (used","children if type=directory. A directory is composed of a list and may contain","the path currently being checked (used to build paths recursively for error msg)","the expected element. May be defined by regular expression @param actualElement the actual","@param nameOfBundle name of the bundle ZIP file \"\"\" print ('Validating bundle structure...')","raise return data def directoryToJSON(directory): \"\"\" Function to convert objects in a given","if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return false if needed","parent directory for the Dockerfile :param str docker_url: the docker registry VIP accessible","is composed of a list and may contain files and/or directories. @param directory","def openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param jsonFile path to","+ \" seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function","verify if an actual list of objects contains an expected element. Helper method","actual objects contain the expected element \"\"\" # if actual is a dict","import json import argparse import zipfile import shutil import requests import datetime import","dict, it may contain children if type=directory. A directory is composed of a","a list and may contain files and/or directories. @param directory directory to convert","dict then verify it and its children if type(actual) is dict: isMatched =","defined structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle name","\"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] ==","functional username for the docker registry @param pwd the password for the docker","Function to verify if an uploaded bundle is: 1) a valid zip file","actual, currentPath): \"\"\" Function to verify if an uploaded bundle follows IBM defined","valid zip file 2) follows IBM defined structure @param zipDirectory where the bundle","of the image :param str image_name: the name of the image :param str","if actual is a dict then verify it and its children if type(actual)","not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is","global list of error messages to keep track of all error msgs errorMessages","the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile =","contain children if type=directory. A directory is composed of a list and may","for error msg) @param isMatched (only used for recursive calls) @return True if","\"\"\" Function to add error messages to the global list of errorMessages @param","@param isMatched (only used for recursive calls) @return True if the list of","unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip files @param zip_file the","in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def","track of all error msgs errorMessages = [] \"\"\" Collection of Common Functions","os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the","if files/directories match. They must have the same name and must both be","check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the","can be anything then do no further checking else: isMatched = isMatched &","addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match. They","files @param zip_file the zip file to be extracted @param to_dir the destination","'-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp","if they don't match then log the error msg and return false if","ZIP file is good. False otherwise. \"\"\" ret = True try: the_zip_file =","needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to","Could not proceed with deployment.\") # clean up unzipped stuff and package structure","JSON file \"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The","nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure","Function to adds error messages to the global array. @param expected the expected","the bundle @param expectedElement the expected element to find in the bundle @param","working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo,","username for the docker registry @param pwd the password for the docker registry","the docker cert location # is figured out and we specify it in","get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of an image based on","registry VIP accessible from the mesos slaves :param str image_namespace: the name of","the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file","# verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory,","children in a directory is a list composed of more files/directories d['children'] =","actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True return ret","openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file","parent object is always a dict, it may contain children if type=directory. A","\"\"\" Function to verify if an uploaded bundle is: 1) a valid zip","data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid: \" +","('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try:","verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded bundle is: 1) a","if matchedPosition != -1: # if they match then verify their children too","messages to the global list of errorMessages @param errorMessage the error message to","# Format the FROM command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace","or directory was found if expected['type'] == 'directory': currentPath = currentPath + actual['name']","verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match then log","[] \"\"\" Collection of Common Functions used by Build Scripts A collection of","slaves :param str image_namespace: the name of the image :param str image_name: the","requests @param url the url of the REST call @param usr the functional","Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment","isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath):","# copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if","usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python module requests @param url","ZIP file recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo","else : # if they don't match then log the error msg and","position elif type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)):","\"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM \" + docker_url +","+ image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) #","in a directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x))","matched position elif type(actual) is list: matchedPosition = -1 for i in range(0,","structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy","{ 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker","into JSON form. The parent object is always a dict, it may contain","unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file","using the correct docker registry URL associated with the datacenter this script is","actualElement): \"\"\" Function to check if files/directories match. They must have the same","defined structure @param expected the JSON representation of the IBM defined structure @param","May be defined by regular expression @param actualElement the actual element \"\"\" ret","@param usr the functional username for the docker registry @param pwd the password","extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close()","directoryToJSON(directory): \"\"\" Function to convert objects in a given directory into JSON form.","on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create","IBM defined structure. False otherwise. \"\"\" isMatched = True if type(expected) is dict:","the actual files and directories in the bundle @param expectedElement the expected element","expected element @param currentPath the current path we are on that has the","@param directory directory to convert @return JSON representation of a directory \"\"\" d","or directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False","d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify","latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp <","os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo,","else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not","addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could not proceed with deployment.\")","ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory,","except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\") bundleIsGood","True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new","is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd),","auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version","type. @param expectedElement the expected element. May be defined by regular expression @param","'300' print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\")","# check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert","for error msg) @return True if structure of the uploaded bundle follows IBM","the ZIP file is good. False otherwise. \"\"\" ret = True try: the_zip_file","for the docker registry functional user @return a JSON response \"\"\" headers =","\" + docker_url + image_namespace + \"/\" + image_name + \":\" + image_tag_latest",":returns: None \"\"\" # Form the path for the Dockerfile based on the","False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded","the FROM command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace + \"/\"","file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url,","structure. Could not proceed with deployment.\") # clean up unzipped stuff and package","the destination directory to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\")","type(expected) is dict: if matches(expected,actual): # a matching file or directory was found","is list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition","Could not proceed with deployment.\") # out put report value , join all","to unzip a ZIP file recursively @param zipFileName the zip file to be","proceed with deployment.\") # clean up unzipped stuff and package structure Json -------------------------------------------------","return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded bundle","defined to be the one with the highest version number (vX) and the","if the contents of the directory can be anything then do no further","> str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp","of the IBM defined structure @param actual the JSON representation of the actual","@param registry_path docker registry path @param usr the functional username for the docker","d = {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type']","image_name + \":\" + image_tag_latest # Write the FROM command string to the","latest image tag \"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr,","# is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr,","bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param","bundle does not meet predefined structure. Could not proceed with deployment.\") # clean","str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag","the Dockerfile based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir,","if type(expected) is dict: if matches(expected,actual): # a matching file or directory was","registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag","# log error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement,","good. False otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile =","= isMatched & True # if the contents of the directory can be","the loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file)","Helper method to verifyBundleStructure. @param actual list of the actual files and directories","GET requests using external Python module requests @param url the url of the","directory can be anything then do no further checking else: isMatched = isMatched","isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the global list","string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def","Function to unzip a ZIP file recursively @param zipFileName the zip file to","may contain children if type=directory. A directory is composed of a list and","type=directory. A directory is composed of a list and may contain files and/or","image :param str image_tag_latest: the latest version tag of the base image :returns:","if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def","checked (used to build paths recursively for error msg) @param isMatched (only used","a directory \"\"\" d = {'name': os.path.basename(directory)} # the parent object is dict","bundle @param expectedElement the expected element to find in the bundle @param currentPath","str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try:","must both be the same type. @param expectedElement the expected element. May be","they don't match then log the error msg and return false if expectedElement['fail-if-not-found']","good or bad @param filePath the zip file to be tested @return True","its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) #",": # if they don't match then log the error msg and return","+\" is missing from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return","file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path:","of the actual files and directories in the bundle @param expectedElement the expected","for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x )","for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return","tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag","if so get the matched position elif type(actual) is list: matchedPosition = -1","zipfile import shutil import requests import datetime import re import operator import unicodedata","error messages to keep track of all error msgs errorMessages = [] \"\"\"","def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip files @param zip_file","Build Scripts A collection of common functions shared by each individual build scripts.","the parent directory for the Dockerfile :param str docker_url: the docker registry VIP","nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo","and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the","not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp =",", join all the messages together print (\"report=[\" + \". \".join(str(x) for x","deployment.\") # out put report value , join all the messages together print","actualElement the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not","directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively @param zipFileName the zip","= {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] =","otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if","the actual structure of the uploaded bundle @param currentPath the path currently being","The parent object is always a dict, it may contain children if type=directory.","add error messages to the global list of errorMessages @param errorMessage the error","'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker cert","for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition","< str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if","to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir)","external Python module requests @param url the url of the REST call @param","highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry","= datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version","the contents of the directory can be anything then do no further checking","found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \"/\" if","password for the docker registry functional user @return the latest image tag \"\"\"","of Common Functions used by Build Scripts A collection of common functions shared","directory directory to convert @return JSON representation of a directory \"\"\" d =","directory is composed of a list and may contain files and/or directories. @param","files/directories match. They must have the same name and must both be the","str docker_url: the docker registry VIP accessible from the mesos slaves :param str","is good or bad @param filePath the zip file to be tested @return","if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo,","dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif latest_version <","the list of actual objects contain the expected element \"\"\" # if actual","def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of an image based","to the JSON file @return the loaded JSON file \"\"\" try: with open(jsonFile)","the error msg and return false if expectedElement['fail-if-not-found'] == \"yes\": # log error","False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages","to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName)","list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched)","tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp","currentPath): \"\"\" Function to adds error messages to the global array. @param expected","file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo)","os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure =","str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version: continue","its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one","URL associated with the datacenter this script is being run on :param str","list and may contain files and/or directories. @param directory directory to convert @return","try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret","bundle could not be unzipped. Could not proceed with deployment.\") # out put","\"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path: \\\"\" + currentPath","registry_path docker registry path @param usr the functional username for the docker registry","version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition,","currentPath the current path we are on that has the missing file or",", currentPath) else : # if they don't match then log the error","logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to","<filename>scripts/common_lib/build_lib.py #!/usr/bin/env python3.6 import os import subprocess import json import argparse import zipfile","bundle follows IBM defined structure. False otherwise. \"\"\" isMatched = True if type(expected)","@param expectedElement the expected element to find in the bundle @param currentPath the","= False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error","functional user @return the latest image tag \"\"\" tag_list_url = registry_path + '/tags/list'","copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not","x in os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath):","@param expectedElement the expected element. May be defined by regular expression @param actualElement","currentPath) # if actual is a list then find out if they match","+ image_namespace + \"/\" + image_name + \":\" + image_tag_latest # Write the","open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not","messages to keep track of all error msgs errorMessages = [] \"\"\" Collection","False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret =","list of actual objects contain the expected element \"\"\" # if actual is","tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue","caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path,","tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version","based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") #","recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo the destination","list of objects contains an expected element. Helper method to verifyBundleStructure. @param actual","bundle is: 1) a valid zip file 2) follows IBM defined structure @param","if expected['children'] == \"_any\": isMatched = isMatched & True # if the contents","They must have the same name and must both be the same type.","instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\"","os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0]))","user @return a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } #","JSON representation of the actual structure of the uploaded bundle @param currentPath the","bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo)","# Form the path for the Dockerfile based on the parent of the","paths recursively for error msg) @return True if structure of the uploaded bundle","i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition !=","name and must both be the same type. @param expectedElement the expected element.","Format the FROM command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace +","os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return","the REST call @param usr the functional username for the docker registry @param","out put report value , join all the messages together print (\"report=[\" +","of the REST call @param usr the functional username for the docker registry","structure @param actual the JSON representation of the actual structure of the uploaded","the uploaded bundle follows IBM defined structure. False otherwise. \"\"\" isMatched = True","user @return the latest image tag \"\"\" tag_list_url = registry_path + '/tags/list' request","= isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a","is good. False otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile","print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip):","seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open"],"string":"[\n \"new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip,\",\n \"for the docker registry @param pwd the password for the docker registry functional\",\n \"zip_file the zip file to be extracted @param to_dir the destination directory to\",\n \"unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle\",\n \"for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle\",\n \"used for recursive calls) @return True if the list of actual objects contain\",\n \"= \\\"directory\\\" # directory may have children # the children in a directory\",\n \"# if actual is a dict then verify it and its children if\",\n \"follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is\",\n \"structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not\",\n \"Dockerfile :param str docker_url: the docker registry VIP accessible from the mesos slaves\",\n \"== 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\":\",\n \"to build paths recursively for error msg) @param isMatched (only used for recursive\",\n \"def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the global list of\",\n \"does not meet predefined structure. Could not proceed with deployment.\\\") # clean up\",\n \"} # TEMP: Remove the suppressed verification once the docker cert location #\",\n \"openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param jsonFile path to the\",\n \"== \\\"yes\\\": # log error msg and return false if needed isMatched =\",\n \"= tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting\",\n \"of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile\",\n \"false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return false if\",\n \"else: ret = True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle):\",\n \"zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory\",\n \"import datetime import re import operator import unicodedata # global list of error\",\n \"is not valid: \\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function\",\n \"convert @return JSON representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} #\",\n \"a ZIP file is good or bad @param filePath the zip file to\",\n \"dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT']\",\n \"True # if the contents of the directory can be anything then do\",\n \"is a dict then verify it and its children if type(actual) is dict:\",\n \"(only used for recursive calls) @return True if the list of actual objects\",\n \"uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if\",\n \"unzipped. Could not proceed with deployment.\\\") # out put report value , join\",\n \"bundle follows IBM defined structure @param expected the JSON representation of the IBM\",\n \"location # is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url,\",\n \"is a list then find out if they match anywhere, if so get\",\n \"subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def\",\n \"a list then find out if they match anywhere, if so get the\",\n \"build paths recursively for error msg) @return True if structure of the uploaded\",\n \"isMatched = isMatched & True # if the contents of the directory can\",\n \"verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\"))\",\n \"\\\"\\\"\\\" # if actual is a dict then verify it and its children\",\n \"a JSON file @param jsonFile path to the JSON file @return the loaded\",\n \"be the one with the highest version number (vX) and the latest timestamp\",\n \"pwd the password for the docker registry functional user @return a JSON response\",\n \"tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list\",\n \"\\\"\\\"\\\" Function to convert objects in a given directory into JSON form. The\",\n \"unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying\",\n \"name of the image :param str image_name: the name of the image :param\",\n \"url of the REST call @param usr the functional username for the docker\",\n \"not valid: \\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to\",\n \"too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if\",\n \"file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle)\",\n \"= directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure,\",\n \"JSON representation of the IBM defined structure @param actual the JSON representation of\",\n \"no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do\",\n \"match. They must have the same name and must both be the same\",\n \"the global array. @param expected the expected element @param currentPath the current path\",\n \"\\\"directory\\\" # directory may have children # the children in a directory is\",\n \"break if matchedPosition != -1: # if they match then verify their children\",\n \"to the global list of errorMessages @param errorMessage the error message to add\",\n \"isMatched): \\\"\\\"\\\" Function to verify if an actual list of objects contains an\",\n \"= dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip\",\n \"be the same type. @param expectedElement the expected element. May be defined by\",\n \"FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" +\",\n \"matches(expected,actual): # a matching file or directory was found if expected['type'] == 'directory':\",\n \"Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM\",\n \"elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag\",\n \"@param filePath the zip file to be tested @return True if the ZIP\",\n \"scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python\",\n \"put report value , join all the messages together print (\\\"report=[\\\" + \\\".\",\n \"True if the list of actual objects contain the expected element \\\"\\\"\\\" #\",\n \"adds error messages to the global array. @param expected the expected element @param\",\n \"vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one with the\",\n \"bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo):\",\n \"expectedElement the expected element. May be defined by regular expression @param actualElement the\",\n \"range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected,\",\n \"JSON file is not valid: \\\" + jsonFile) raise return data def directoryToJSON(directory):\",\n \"True if type(expected) is dict: if matches(expected,actual): # a matching file or directory\",\n \"docker registry URL associated with the datacenter this script is being run on\",\n \"logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in range(0,len(expected)): isMatched\",\n \"currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle follows IBM defined structure\",\n \"docker registry VIP accessible from the mesos slaves :param str image_namespace: the name\",\n \"that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is\",\n \"\\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM\",\n \"uploaded bundle follows IBM defined structure @param expected the JSON representation of the\",\n \"import unicodedata # global list of error messages to keep track of all\",\n \"A directory is composed of a list and may contain files and/or directories.\",\n \"the one with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm).\",\n \"script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\")\",\n \"a directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for\",\n \"expected element \\\"\\\"\\\" # if actual is a dict then verify it and\",\n \"for recursive calls) @return True if the list of actual objects contain the\",\n \"def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual list\",\n \"common functions shared by each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd):\",\n \"@param zip_file the zip file to be extracted @param to_dir the destination directory\",\n \"path we are on that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A\",\n \"out if they match anywhere, if so get the matched position elif type(actual)\",\n \"isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function\",\n \"actual is a dict then verify it and its children if type(actual) is\",\n \"list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else:\",\n \"a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile\",\n \"the functional username for the docker registry @param pwd the password for the\",\n \"of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object is\",\n \"if the ZIP file is good. False otherwise. \\\"\\\"\\\" ret = True try:\",\n \"+ os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile):\",\n \"the latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest,\",\n \"continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except\",\n \"except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if\",\n \"cert location # is figured out and we specify it in REQUESTS_CA_BUNDLE return\",\n \"could not be unzipped. Could not proceed with deployment.\\\") # out put report\",\n \"return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given directory\",\n \"expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return false if needed isMatched\",\n \"for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual,\",\n \"then find out if they match anywhere, if so get the matched position\",\n \"# global list of error messages to keep track of all error msgs\",\n \"def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to the global array.\",\n \"to build paths recursively for error msg) @return True if structure of the\",\n \"= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp:\",\n \"the expected element \\\"\\\"\\\" # if actual is a dict then verify it\",\n \"dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError:\",\n \"+ '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in\",\n \"directory into JSON form. The parent object is always a dict, it may\",\n \"of common functions shared by each individual build scripts. \\\"\\\"\\\" def get(url, usr,\",\n \"if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo):\",\n \"@param pwd the password for the docker registry functional user @return the latest\",\n \"all error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used by\",\n \":param str image_namespace: the name of the image :param str image_name: the name\",\n \"the password for the docker registry functional user @return the latest image tag\",\n \"update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in\",\n \"(vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr\",\n \"objects in a given directory into JSON form. The parent object is always\",\n \"= [] \\\"\\\"\\\" Collection of Common Functions used by Build Scripts A collection\",\n \"extracted @param to_dir the destination directory to extract the zip file to \\\"\\\"\\\"\",\n \"of the image :param str image_tag_latest: the latest version tag of the base\",\n \"# Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT\",\n \"json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash,\",\n \"matching file or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath)\",\n \"recursive calls) @return True if the list of actual objects contain the expected\",\n \"os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure\",\n \"actual['children'], currentPath) # do further checking else: # a matching file or directory\",\n \"False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could\",\n \"# a matching file or directory was found if expected['type'] == 'directory': currentPath\",\n \"bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could not proceed with\",\n \"type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is\",\n \"of the uploaded bundle @param currentPath the path currently being checked (used to\",\n \"actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and\",\n \"or bad @param filePath the zip file to be tested @return True if\",\n \"errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively\",\n \"in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory)\",\n \"Function to open a JSON file @param jsonFile path to the JSON file\",\n \"unzip function for extracting zip files @param zip_file the zip file to be\",\n \"and its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath)\",\n \"open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to\",\n \"len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if\",\n \"\\\"\\\"\\\" Retrieve the latest version of an image based on its tags: vX-YYYYMMDD-HHmm.\",\n \"the name of the image :param str image_name: the name of the image\",\n \"print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") #\",\n \"build paths recursively for error msg) @param isMatched (only used for recursive calls)\",\n \"object is always a dict, it may contain children if type=directory. A directory\",\n \"destination directory to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as\",\n \"# the parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory\",\n \"in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\"\",\n \"!= -1: # if they match then verify their children too isMatched =\",\n \"their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else :\",\n \"latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files @param\",\n \"expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k\",\n \"# out put report value , join all the messages together print (\\\"report=[\\\"\",\n \"zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret = False else:\",\n \"the docker registry VIP accessible from the mesos slaves :param str image_namespace: the\",\n \"# convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\")\",\n \"jsonFile path to the JSON file @return the loaded JSON file \\\"\\\"\\\" try:\",\n \"then log the error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": #\",\n \"d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d\",\n \"# update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x\",\n \"\\\"\\\"\\\" Function to unzip a ZIP file recursively @param zipFileName the zip file\",\n \"d['type'] = \\\"directory\\\" # directory may have children # the children in a\",\n \"zip files @param zip_file the zip file to be extracted @param to_dir the\",\n \"= os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format\",\n \"into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo)\",\n \"json import argparse import zipfile import shutil import requests import datetime import re\",\n \"actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure.\",\n \"anything then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'],\",\n \"be defined by regular expression @param actualElement the actual element \\\"\\\"\\\" ret =\",\n \"\\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ----------------------------------------------------------------------------------\",\n \"follows IBM defined structure @param expected the JSON representation of the IBM defined\",\n \"type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k],\",\n \"\\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\" +\",\n \"of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True\",\n \"may have children # the children in a directory is a list composed\",\n \"\\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list =\",\n \"composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type']\",\n \"os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if\",\n \"bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ #\",\n \"docker registry @param pwd the password for the docker registry functional user @return\",\n \"registry path @param usr the functional username for the docker registry @param pwd\",\n \"Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open\",\n \"latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic\",\n \"expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children'] ==\",\n \"ZIP file is good or bad @param filePath the zip file to be\",\n \"usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an image based on its\",\n \"variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client:\",\n \"structure of the uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched\",\n \"using external Python module requests @param url the url of the REST call\",\n \"dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the\",\n \"by Build Scripts A collection of common functions shared by each individual build\",\n \"error msg) @return True if structure of the uploaded bundle follows IBM defined\",\n \"def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file is good or\",\n \"uploaded bundle does not meet predefined structure. Could not proceed with deployment.\\\") #\",\n \"and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def\",\n \"python3.6 import os import subprocess import json import argparse import zipfile import shutil\",\n \"url the url of the REST call @param usr the functional username for\",\n \"the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd =\",\n \"open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url\",\n \"currentPath the path currently being checked (used to build paths recursively for error\",\n \"bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: #\",\n \"< dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir):\",\n \"\\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match.\",\n \"datacenter this script is being run on :param str dockerfile_parent_dir: path to the\",\n \"file recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo the\",\n \"None: ret = False else: ret = True except: ret = False return\",\n \"usr the functional username for the docker registry @param pwd the password for\",\n \"ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is\",\n \"try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag\",\n \"and may contain files and/or directories. @param directory directory to convert @return JSON\",\n \"verification once the docker cert location # is figured out and we specify\",\n \"with the datacenter this script is being run on :param str dockerfile_parent_dir: path\",\n \"@param expected the expected element @param currentPath the current path we are on\",\n \"the expected element @param currentPath the current path we are on that has\",\n \"registry URL associated with the datacenter this script is being run on :param\",\n \"isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to the global\",\n \"import zipfile import shutil import requests import datetime import re import operator import\",\n \"JSON file @param jsonFile path to the JSON file @return the loaded JSON\",\n \"method to verifyBundleStructure. @param actual list of the actual files and directories in\",\n \"on :param str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param\",\n \"'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker cert location\",\n \"is being run on :param str dockerfile_parent_dir: path to the parent directory for\",\n \"path currently being checked (used to build paths recursively for error msg) @param\",\n \".zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile =\",\n \"Function to convert objects in a given directory into JSON form. The parent\",\n \"the same name and must both be the same type. @param expectedElement the\",\n \"def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle follows\",\n \"an uploaded bundle is: 1) a valid zip file 2) follows IBM defined\",\n \"+ \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write the FROM command\",\n \"convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if\",\n \"uploaded bundle could not be unzipped. Could not proceed with deployment.\\\") # out\",\n \"to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout():\",\n \"if matches(expected,actual): # a matching file or directory was found if expected['type'] ==\",\n \"= \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if\",\n \"except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify\",\n \"& True # if the contents of the directory can be anything then\",\n \"verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file\",\n \"to adds error messages to the global array. @param expected the expected element\",\n \"the matched position elif type(actual) is list: matchedPosition = -1 for i in\",\n \"of all error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used\",\n \"element. Helper method to verifyBundleStructure. @param actual list of the actual files and\",\n \"Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout\",\n \"bundle @param currentPath the path currently being checked (used to build paths recursively\",\n \"test if a ZIP file is good or bad @param filePath the zip\",\n \"an uploaded bundle follows IBM defined structure @param expected the JSON representation of\",\n \"os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\"\",\n \"to find in the bundle @param currentPath the path currently being checked (used\",\n \"version tag of the base image :returns: None \\\"\\\"\\\" # Form the path\",\n \"currentPath) return False if type(expected) is list: for k in range(0,len(expected)): isMatched =\",\n \"= open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" +\",\n \"mesos slaves :param str image_namespace: the name of the image :param str image_name:\",\n \"= tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version\",\n \"the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker\",\n \"checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking\",\n \"Scripts A collection of common functions shared by each individual build scripts. \\\"\\\"\\\"\",\n \"expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched):\",\n \"except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\" + jsonFile) raise return\",\n \"Collection of Common Functions used by Build Scripts A collection of common functions\",\n \"matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i\",\n \"response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed\",\n \"match then log the error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\":\",\n \"----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) #\",\n \"addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the global list of errorMessages\",\n \"= dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif latest_version\",\n \"latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:\",\n \"# TEMP: Remove the suppressed verification once the docker cert location # is\",\n \"directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle))\",\n \"or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\"\",\n \"else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag\",\n \"is located @param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print ('Validating\",\n \"+ \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched & True # if\",\n \"print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\") return bundleIsGood\",\n \"latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif\",\n \"specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr,\",\n \"latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp =\",\n \"each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests\",\n \"command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" + image_name\",\n \"image_tag_latest: the latest version tag of the base image :returns: None \\\"\\\"\\\" #\",\n \":param str docker_url: the docker registry VIP accessible from the mesos slaves :param\",\n \"name of the image :param str image_tag_latest: the latest version tag of the\",\n \"image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker registry URL associated\",\n \"msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used by Build Scripts\",\n \"currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if\",\n \"shutil import requests import datetime import re import operator import unicodedata # global\",\n \"defined by regular expression @param actualElement the actual element \\\"\\\"\\\" ret = False\",\n \"ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp =\",\n \"= False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an\",\n \"the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a\",\n \"currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the\",\n \"with deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could\",\n \"being checked (used to build paths recursively for error msg) @param isMatched (only\",\n \"VIP accessible from the mesos slaves :param str image_namespace: the name of the\",\n \"# Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the\",\n \"global list of errorMessages @param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage)\",\n \"file is good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath)\",\n \"return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file is good\",\n \"\\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module\",\n \"by each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET\",\n \"addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" + currentPath +\",\n \"a dockerfile using the correct docker registry URL associated with the datacenter this\",\n \"& verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match then\",\n \"return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage):\",\n \"return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the\",\n \"\\\"\\\"\\\" isMatched = True if type(expected) is dict: if matches(expected,actual): # a matching\",\n \"is defined to be the one with the highest version number (vX) and\",\n \"for the Dockerfile :param str docker_url: the docker registry VIP accessible from the\",\n \"def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle is: 1)\",\n \"the image :param str image_name: the name of the image :param str image_tag_latest:\",\n \"message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\"\",\n \"Could not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The\",\n \"\\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure =============================================== def\",\n \"the directory can be anything then do no further checking else: isMatched =\",\n \"import shutil import requests import datetime import re import operator import unicodedata #\",\n \"together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\") return\",\n \"zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the\",\n \"was found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\"\",\n \"\\\"\\\"\\\" Collection of Common Functions used by Build Scripts A collection of common\",\n \"the password for the docker registry functional user @return a JSON response \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" Function to check if files/directories match. They must have the same name\",\n \"where the bundle ZIP is located @param nameOfBundle name of the bundle ZIP\",\n \"a matching file or directory was found if expected['type'] == 'directory': currentPath =\",\n \"has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing\",\n \"+ docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest #\",\n \"ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual\",\n \"subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to\",\n \"directories in the bundle @param expectedElement the expected element to find in the\",\n \"tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format)\",\n \"expected['children'] == \\\"_any\\\": isMatched = isMatched & True # if the contents of\",\n \"a dict then verify it and its children if type(actual) is dict: isMatched\",\n \"bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working\",\n \"data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given directory into\",\n \"= False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be unzipped.\",\n \"= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version:\",\n \"\\\"\\\"\\\" Function to test if a ZIP file is good or bad @param\",\n \"the base image :returns: None \\\"\\\"\\\" # Form the path for the Dockerfile\",\n \"error msg) @param isMatched (only used for recursive calls) @return True if the\",\n \"unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood:\",\n \"#check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory =\",\n \"in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1:\",\n \"loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except:\",\n \"otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict: if matches(expected,actual): # a\",\n \"verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined\",\n \"False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not proceed with deployment.\\\")\",\n \"= os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test\",\n \"# directory may have children # the children in a directory is a\",\n \"= openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON\",\n \"the JSON file @return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as\",\n \"structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure\",\n \"messages to the global array. @param expected the expected element @param currentPath the\",\n \"dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM \\\"\",\n \"checking else: # a matching file or directory was not found if expected['fail-if-not-found']\",\n \"then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath)\",\n \"'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched\",\n \"# if they match then verify their children too isMatched = isMatched &\",\n \"registry @param pwd the password for the docker registry functional user @return a\",\n \"error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo):\",\n \"the docker registry functional user @return a JSON response \\\"\\\"\\\" headers = {\",\n \"None and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement,\",\n \"actual list of the actual files and directories in the bundle @param expectedElement\",\n \"have children # the children in a directory is a list composed of\",\n \"import os import subprocess import json import argparse import zipfile import shutil import\",\n \"destination directory to extract the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"):\",\n \"extracted @param directoryToUnzipTo the destination directory to extract the zip file to \\\"\\\"\\\"\",\n \"False if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched &\",\n \"NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version\",\n \"\\\"_any\\\": isMatched = isMatched & True # if the contents of the directory\",\n \"directory was found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] +\",\n \"bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be\",\n \"error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg\",\n \"more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\"\",\n \"except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp\",\n \"is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a\",\n \"tag of the base image :returns: None \\\"\\\"\\\" # Form the path for\",\n \"requests using external Python module requests @param url the url of the REST\",\n \"the latest version tag of the base image :returns: None \\\"\\\"\\\" # Form\",\n \"a matching file or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected,\",\n \"dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children # the\",\n \"to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded\",\n \"IBM defined structure @param actual the JSON representation of the actual structure of\",\n \") unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP\",\n \"if they match anywhere, if so get the matched position elif type(actual) is\",\n \"and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return\",\n \"defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict: if\",\n \"Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd\",\n \"pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of\",\n \"the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None\",\n \"to be the one with the highest version number (vX) and the latest\",\n \"expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails'])\",\n \"matchedPosition = i break if matchedPosition != -1: # if they match then\",\n \"children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if\",\n \"currently being checked (used to build paths recursively for error msg) @param isMatched\",\n \"------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed with\",\n \"addToErrorMessages(\\\"The specified JSON file is not valid: \\\" + jsonFile) raise return data\",\n \"@return True if the list of actual objects contain the expected element \\\"\\\"\\\"\",\n \"(used to build paths recursively for error msg) @return True if structure of\",\n \"tag else: if latest_version > str_version: continue elif latest_version < str_version: latest_version =\",\n \"datetime import re import operator import unicodedata # global list of error messages\",\n \"valid: \\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert\",\n \"def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively @param zipFileName\",\n \"timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username for\",\n \"'/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']:\",\n \"same name and must both be the same type. @param expectedElement the expected\",\n \"the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) +\",\n \"of actual objects contain the expected element \\\"\\\"\\\" # if actual is a\",\n \"\\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The\",\n \"the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\"\",\n \"\\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in range(0,len(expected)):\",\n \"The latest, by definition, is defined to be the one with the highest\",\n \"verify if an uploaded bundle is: 1) a valid zip file 2) follows\",\n \"print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a\",\n \"to check if files/directories match. They must have the same name and must\",\n \"\\\":\\\" + image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd)\",\n \"import operator import unicodedata # global list of error messages to keep track\",\n \"return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded\",\n \"docker cert location # is figured out and we specify it in REQUESTS_CA_BUNDLE\",\n \"# if they don't match then log the error msg and return false\",\n \"in a given directory into JSON form. The parent object is always a\",\n \"name of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood =\",\n \"be tested @return True if the ZIP file is good. False otherwise. \\\"\\\"\\\"\",\n \"further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further\",\n \"bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not\",\n \"= isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\"\",\n \"of a list and may contain files and/or directories. @param directory directory to\",\n \"\\\"\\\"\\\" # Form the path for the Dockerfile based on the parent of\",\n \"argparse import zipfile import shutil import requests import datetime import re import operator\",\n \"convert objects in a given directory into JSON form. The parent object is\",\n \"a given directory into JSON form. The parent object is always a dict,\",\n \"the path for the Dockerfile based on the parent of the caller script\",\n \"structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict: if matches(expected,actual):\",\n \"REST call @param usr the functional username for the docker registry @param pwd\",\n \"a dict, it may contain children if type=directory. A directory is composed of\",\n \"image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close\",\n \"further checking else: # a matching file or directory was not found if\",\n \"ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle is:\",\n \"actual[matchedPosition] , currentPath) else : # if they don't match then log the\",\n \"calls) @return True if the list of actual objects contain the expected element\",\n \"function for extracting zip files @param zip_file the zip file to be extracted\",\n \"latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username\",\n \"os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function\",\n \"os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle\",\n \"in the bundle @param currentPath the path currently being checked (used to build\",\n \"else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else:\",\n \"usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in\",\n \"element \\\"\\\"\\\" # if actual is a dict then verify it and its\",\n \"of errorMessages @param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges\",\n \"the same type. @param expectedElement the expected element. May be defined by regular\",\n \"\\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates\",\n \"a ZIP file recursively @param zipFileName the zip file to be extracted @param\",\n \"\\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched & True # if the\",\n \"= get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-'\",\n \"files and directories in the bundle @param expectedElement the expected element to find\",\n \"matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if they match\",\n \"# ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a\",\n \"ZIP is located @param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print\",\n \"return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively @param\",\n \"str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp\",\n \"representation of the actual structure of the uploaded bundle @param currentPath the path\",\n \"x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a\",\n \"ret = False else: ret = True except: ret = False return ret\",\n \"= False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret\",\n \"registry functional user @return a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json',\",\n \"zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file is good or bad\",\n \"the mesos slaves :param str image_namespace: the name of the image :param str\",\n \"in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\"\",\n \"= json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue str_version,\",\n \"= { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the\",\n \"is always a dict, it may contain children if type=directory. A directory is\",\n \"isMatched & True # if the contents of the directory can be anything\",\n \"directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip\",\n \"expression @param actualElement the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name'])\",\n \"dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function\",\n \"# unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of\",\n \"to be extracted @param to_dir the destination directory to extract the zip file\",\n \"do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) #\",\n \"get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests @param\",\n \"matchedPosition != -1: # if they match then verify their children too isMatched\",\n \"to the global array. @param expected the expected element @param currentPath the current\",\n \"os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify\",\n \"is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children #\",\n \"is not None and expectedElement['type'] == actualElement['type']: ret = True return ret def\",\n \"the name of the image :param str image_tag_latest: the latest version tag of\",\n \"Retrieve the latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The\",\n \"stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory\",\n \"os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children # the children in\",\n \"module requests @param url the url of the REST call @param usr the\",\n \"@return JSON representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the\",\n \"pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests @param url the\",\n \"of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is\",\n \"the destination directory to extract the zip file to \\\"\\\"\\\" # update if\",\n \"str image_namespace: the name of the image :param str image_name: the name of\",\n \"keep track of all error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common\",\n \"actual list of objects contains an expected element. Helper method to verifyBundleStructure. @param\",\n \"return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return false\",\n \"verify it and its children if type(actual) is dict: isMatched = isMatched &\",\n \"msg) @return True if structure of the uploaded bundle follows IBM defined structure.\",\n \"\\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects\",\n \"the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def\",\n \"JSON representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent\",\n \"@param currentPath the path currently being checked (used to build paths recursively for\",\n \"list of error messages to keep track of all error msgs errorMessages =\",\n \"requests import datetime import re import operator import unicodedata # global list of\",\n \"figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers,\",\n \"accessible from the mesos slaves :param str image_namespace: the name of the image\",\n \"don't match then log the error msg and return false if expectedElement['fail-if-not-found'] ==\",\n \"uploaded bundle @param currentPath the path currently being checked (used to build paths\",\n \"return False if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched\",\n \"JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle\",\n \"@param actual list of the actual files and directories in the bundle @param\",\n \"image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker registry URL\",\n \"\\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could\",\n \"add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to\",\n \"os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests @param url the url\",\n \"= os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the\",\n \"get the matched position elif type(actual) is list: matchedPosition = -1 for i\",\n \"\\\" seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to\",\n \"all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages)\",\n \"verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else\",\n \"1) a valid zip file 2) follows IBM defined structure @param zipDirectory where\",\n \"= tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag\",\n \"ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']:\",\n \"str image_tag_latest: the latest version tag of the base image :returns: None \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip\",\n \"file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300\",\n \"zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\"\",\n \"try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except\",\n \"image :param str image_name: the name of the image :param str image_tag_latest: the\",\n \"zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip\",\n \"file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified\",\n \"must have the same name and must both be the same type. @param\",\n \"the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\"\",\n \"@return a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP:\",\n \"with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path\",\n \"@return True if structure of the uploaded bundle follows IBM defined structure. False\",\n \"datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version =\",\n \"it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0])\",\n \"was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected)\",\n \"\\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if\",\n \"True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None:\",\n \"# if the contents of the directory can be anything then do no\",\n \"nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle is: 1) a valid\",\n \"= json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\" + jsonFile)\",\n \"import requests import datetime import re import operator import unicodedata # global list\",\n \"for the Dockerfile based on the parent of the caller script dockerfile_path =\",\n \"isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then\",\n \"list of errorMessages @param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global\",\n \"currently being checked (used to build paths recursively for error msg) @return True\",\n \"on that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\"\",\n \"it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd):\",\n \"os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x\",\n \"is: 1) a valid zip file 2) follows IBM defined structure @param zipDirectory\",\n \"the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set\",\n \"given directory into JSON form. The parent object is always a dict, it\",\n \"currentPath) # do further checking else: # a matching file or directory was\",\n \"follows IBM defined structure @param zipDirectory where the bundle ZIP is located @param\",\n \"they match anywhere, if so get the matched position elif type(actual) is list:\",\n \"False else: ret = True except: ret = False return ret def verifyZipFile(zipDirectory,\",\n \"\\\"\\\"\\\" Generic unzip function for extracting zip files @param zip_file the zip file\",\n \"= verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet\",\n \"directory for the Dockerfile :param str docker_url: the docker registry VIP accessible from\",\n \"directory to extract the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check\",\n \"isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add\",\n \"in the bundle @param expectedElement the expected element to find in the bundle\",\n \"to convert objects in a given directory into JSON form. The parent object\",\n \"actual files and directories in the bundle @param expectedElement the expected element to\",\n \"\\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood\",\n \"Creates a dockerfile using the correct docker registry URL associated with the datacenter\",\n \"True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to\",\n \"structure @param expected the JSON representation of the IBM defined structure @param actual\",\n \"an expected element. Helper method to verifyBundleStructure. @param actual list of the actual\",\n \"objects contains an expected element. Helper method to verifyBundleStructure. @param actual list of\",\n \"def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given directory into JSON\",\n \"filePath the zip file to be tested @return True if the ZIP file\",\n \"ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to\",\n \"of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure =\",\n \"shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\\\")\",\n \"currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched &\",\n \"as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid:\",\n \"found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list:\",\n \"# Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command\",\n \"def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct\",\n \"JSON form. The parent object is always a dict, it may contain children\",\n \"@param directoryToUnzipTo the destination directory to extract the zip file to \\\"\\\"\\\" #\",\n \"zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using\",\n \"\\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON\",\n \"tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\"\",\n \"zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir,\",\n \"element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type']\",\n \"True if structure of the uploaded bundle follows IBM defined structure. False otherwise.\",\n \"\\\"\\\"\\\" Creates a dockerfile using the correct docker registry URL associated with the\",\n \"i break if matchedPosition != -1: # if they match then verify their\",\n \"badFile is not None: ret = False else: ret = True except: ret\",\n \"Function to add error messages to the global list of errorMessages @param errorMessage\",\n \"of the uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched =\",\n \"with deployment.\\\") # clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo)\",\n \"None \\\"\\\"\\\" # Form the path for the Dockerfile based on the parent\",\n \"to verify if an uploaded bundle follows IBM defined structure @param expected the\",\n \"# do further checking else: # a matching file or directory was not\",\n \"# if actual is a list then find out if they match anywhere,\",\n \"if actual is a list then find out if they match anywhere, if\",\n \"from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement):\",\n \"and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def\",\n \"directory may have children # the children in a directory is a list\",\n \"the bundle ZIP is located @param nameOfBundle name of the bundle ZIP file\",\n \"is missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def\",\n \"@param actual the JSON representation of the actual structure of the uploaded bundle\",\n \"---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check\",\n \"to be tested @return True if the ZIP file is good. False otherwise.\",\n \"actual structure of the uploaded bundle @param currentPath the path currently being checked\",\n \"file @return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data\",\n \"if latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp\",\n \"\\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name,\",\n \"headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once\",\n \"(used to build paths recursively for error msg) @param isMatched (only used for\",\n \"the docker registry functional user @return the latest image tag \\\"\\\"\\\" tag_list_url =\",\n \"to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function\",\n \"check if files/directories match. They must have the same name and must both\",\n \"isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to\",\n \"expectedElement the expected element to find in the bundle @param currentPath the path\",\n \"if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try:\",\n \"it may contain children if type=directory. A directory is composed of a list\",\n \"directories. @param directory directory to convert @return JSON representation of a directory \\\"\\\"\\\"\",\n \"if type=directory. A directory is composed of a list and may contain files\",\n \"the datacenter this script is being run on :param str dockerfile_parent_dir: path to\",\n \"= currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched\",\n \"composed of a list and may contain files and/or directories. @param directory directory\",\n \"@param expected the JSON representation of the IBM defined structure @param actual the\",\n \"verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual list of\",\n \"expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error\",\n \"latest, by definition, is defined to be the one with the highest version\",\n \"= registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for\",\n \"children # the children in a directory is a list composed of more\",\n \":param str image_name: the name of the image :param str image_tag_latest: the latest\",\n \"directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object is dict if\",\n \"and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath,\",\n \"run on :param str dockerfile_parent_dir: path to the parent directory for the Dockerfile\",\n \"return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match. They must\",\n \"import argparse import zipfile import shutil import requests import datetime import re import\",\n \"the image :param str image_tag_latest: the latest version tag of the base image\",\n \"this script is being run on :param str dockerfile_parent_dir: path to the parent\",\n \"to convert @return JSON representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)}\",\n \"-1: # if they match then verify their children too isMatched = isMatched\",\n \"= True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify\",\n \"= -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break\",\n \"HTTP/HTTPS GET requests using external Python module requests @param url the url of\",\n \"file or directory was found if expected['type'] == 'directory': currentPath = currentPath +\",\n \"latest version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by\",\n \"a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)]\",\n \"\\\"yes\\\": # log error msg and return false if needed isMatched = False\",\n \"if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for\",\n \"to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client: \\\"\",\n \"directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" +\",\n \"@param pwd the password for the docker registry functional user @return a JSON\",\n \"element. May be defined by regular expression @param actualElement the actual element \\\"\\\"\\\"\",\n \"(YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional username for the\",\n \"is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in\",\n \"meet predefined structure. Could not proceed with deployment.\\\") # clean up unzipped stuff\",\n \"\\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an\",\n \"password for the docker registry functional user @return a JSON response \\\"\\\"\\\" headers\",\n \"build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external\",\n \"msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and\",\n \"image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write the FROM\",\n \"requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest\",\n \"isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: #\",\n \"@param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return\",\n \"& verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to\",\n \"Form the path for the Dockerfile based on the parent of the caller\",\n \"def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests\",\n \"isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they\",\n \"msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched\",\n \"is not None: ret = False else: ret = True except: ret =\",\n \"errorMessages @param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage])\",\n \"form. The parent object is always a dict, it may contain children if\",\n \"the current path we are on that has the missing file or directory\",\n \"import re import operator import unicodedata # global list of error messages to\",\n \"import subprocess import json import argparse import zipfile import shutil import requests import\",\n \"+ \\\":\\\" + image_tag_latest # Write the FROM command string to the Dockerfile\",\n \"\\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile\",\n \"file to be extracted @param to_dir the destination directory to extract the zip\",\n \"\\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\")\",\n \"======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON\",\n \"IBM defined structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle\",\n \"currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages\",\n \"to test if a ZIP file is good or bad @param filePath the\",\n \"if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo,\",\n \"dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list\",\n \"@param jsonFile path to the JSON file @return the loaded JSON file \\\"\\\"\\\"\",\n \"= os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath):\",\n \"report value , join all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x)\",\n \"nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture\",\n \"occurred while verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood = False\",\n \"the url of the REST call @param usr the functional username for the\",\n \"to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files @param zip_file the zip\",\n \"both be the same type. @param expectedElement the expected element. May be defined\",\n \"dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str docker_url: the\",\n \"expected element to find in the bundle @param currentPath the path currently being\",\n \"missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the\",\n \"return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to the\",\n \"not proceed with deployment.\\\") # out put report value , join all the\",\n \"error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return\",\n \"they match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition]\",\n \"call @param usr the functional username for the docker registry @param pwd the\",\n \"re import operator import unicodedata # global list of error messages to keep\",\n \"image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to\",\n \"@param to_dir the destination directory to extract the zip file to \\\"\\\"\\\" with\",\n \"type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]):\",\n \"to the parent directory for the Dockerfile :param str docker_url: the docker registry\",\n \"if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual\",\n \"latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file,\",\n \"set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify\",\n \"False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict: if matches(expected,actual): #\",\n \"with deployment.\\\") # out put report value , join all the messages together\",\n \"@return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request =\",\n \"individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using\",\n \"we are on that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+\",\n \"msg) @param isMatched (only used for recursive calls) @return True if the list\",\n \"client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure ===============================================\",\n \"directory to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref:\",\n \"functional user @return a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', }\",\n \"array. @param expected the expected element @param currentPath the current path we are\",\n \"str image_name: the name of the image :param str image_tag_latest: the latest version\",\n \"DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for\",\n \"+ actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched & True\",\n \"if a ZIP file is good or bad @param filePath the zip file\",\n \"path currently being checked (used to build paths recursively for error msg) @return\",\n \"= False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not proceed with\",\n \"children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : #\",\n \"if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True\",\n \"Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param jsonFile\",\n \"nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood\",\n \"if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: # if they\",\n \"= isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't\",\n \"unzip a ZIP file recursively @param zipFileName the zip file to be extracted\",\n \"zip file to be extracted @param directoryToUnzipTo the destination directory to extract the\",\n \"being checked (used to build paths recursively for error msg) @return True if\",\n \"parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have\",\n \"for tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp\",\n \"contain the expected element \\\"\\\"\\\" # if actual is a dict then verify\",\n \"directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x\",\n \"dict: if matches(expected,actual): # a matching file or directory was found if expected['type']\",\n \"+ currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check\",\n \"file or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return\",\n \"functions shared by each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\"\",\n \"REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve\",\n \"the zip file to be tested @return True if the ZIP file is\",\n \"get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not\",\n \"messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\")\",\n \"= \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\"\",\n \"else: # a matching file or directory was not found if expected['fail-if-not-found'] ==\",\n \"parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile\",\n \"def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match. They must have\",\n \"of error messages to keep track of all error msgs errorMessages = []\",\n \"expected element. Helper method to verifyBundleStructure. @param actual list of the actual files\",\n \"os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function\",\n \"os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory -----------------------------------------------------------\",\n \"object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children\",\n \"k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched\",\n \"bad @param filePath the zip file to be tested @return True if the\",\n \"#!/usr/bin/env python3.6 import os import subprocess import json import argparse import zipfile import\",\n \"tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text)\",\n \"pwd the password for the docker registry functional user @return the latest image\",\n \"os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" #\",\n \"@param zipFileName the zip file to be extracted @param directoryToUnzipTo the destination directory\",\n \"bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle\",\n \"to keep track of all error msgs errorMessages = [] \\\"\\\"\\\" Collection of\",\n \"the expected element to find in the bundle @param currentPath the path currently\",\n \"tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return\",\n \"zip file to be extracted @param to_dir the destination directory to extract the\",\n \"join all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in\",\n \"out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False)\",\n \"clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred\",\n \"json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\" + jsonFile) raise\",\n \"defined structure @param actual the JSON representation of the actual structure of the\",\n \"if an uploaded bundle is: 1) a valid zip file 2) follows IBM\",\n \"latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp =\",\n \"directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure,\",\n \"be extracted @param directoryToUnzipTo the destination directory to extract the zip file to\",\n \"= True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not\",\n \"-1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if\",\n \"= tag else: if latest_version > str_version: continue elif latest_version < str_version: latest_version\",\n \"script is being run on :param str dockerfile_parent_dir: path to the parent directory\",\n \"= '300' print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\"\",\n \"return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files\",\n \"unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively @param zipFileName the\",\n \"to verify if an actual list of objects contains an expected element. Helper\",\n \"the children in a directory is a list composed of more files/directories d['children']\",\n \"not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could not proceed\",\n \"zip file to be tested @return True if the ZIP file is good.\",\n \"jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a\",\n \"= zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret = False\",\n \"directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The\",\n \"is dict: if matches(expected,actual): # a matching file or directory was found if\",\n \"of the base image :returns: None \\\"\\\"\\\" # Form the path for the\",\n \"== \\\"_any\\\": isMatched = isMatched & True # if the contents of the\",\n \"being run on :param str dockerfile_parent_dir: path to the parent directory for the\",\n \"find out if they match anywhere, if so get the matched position elif\",\n \"docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker registry\",\n \"located @param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle\",\n \"the uploaded bundle @param currentPath the path currently being checked (used to build\",\n \"errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used by Build Scripts A\",\n \"then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath)\",\n \"actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function\",\n \"version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path\",\n \"if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children']\",\n \"dockerfile using the correct docker registry URL associated with the datacenter this script\",\n \"docker registry functional user @return a JSON response \\\"\\\"\\\" headers = { 'Accept':\",\n \"if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children # the children\",\n \"latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =\",\n \"= dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp\",\n \"+ jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in\",\n \"with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is\",\n \"= os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory\",\n \"re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True return\",\n \"if structure of the uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\"\",\n \"errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def\",\n \"os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] +\",\n \"isMatched = True if type(expected) is dict: if matches(expected,actual): # a matching file\",\n \"# clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception\",\n \"and/or directories. @param directory directory to convert @return JSON representation of a directory\",\n \"a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove\",\n \"to verifyBundleStructure. @param actual list of the actual files and directories in the\",\n \"path to the parent directory for the Dockerfile :param str docker_url: the docker\",\n \"deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not\",\n \"\\\"\\\"\\\" Function to adds error messages to the global array. @param expected the\",\n \"= [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def\",\n \"JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the\",\n \"extracting zip files @param zip_file the zip file to be extracted @param to_dir\",\n \"and directories in the bundle @param expectedElement the expected element to find in\",\n \"are on that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type']\",\n \"objects contain the expected element \\\"\\\"\\\" # if actual is a dict then\",\n \"@return True if the ZIP file is good. False otherwise. \\\"\\\"\\\" ret =\",\n \"shared by each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS\",\n \"docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write\",\n \"the JSON representation of the actual structure of the uploaded bundle @param currentPath\",\n \"of the actual structure of the uploaded bundle @param currentPath the path currently\",\n \"\\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory):\",\n \"of objects contains an expected element. Helper method to verifyBundleStructure. @param actual list\",\n \"an actual list of objects contains an expected element. Helper method to verifyBundleStructure.\",\n \"the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not\",\n \"list of the actual files and directories in the bundle @param expectedElement the\",\n \"by regular expression @param actualElement the actual element \\\"\\\"\\\" ret = False if\",\n \"the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable\",\n \"deployment.\\\") # clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except:\",\n \"latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version >\",\n \"log error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath)\",\n \"image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd)\",\n \"if badFile is not None: ret = False else: ret = True except:\",\n \"if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could not\",\n \"file to be extracted @param directoryToUnzipTo the destination directory to extract the zip\",\n \"if they match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement,\",\n \"the suppressed verification once the docker cert location # is figured out and\",\n \"currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual list of objects contains\",\n \"the bundle @param currentPath the path currently being checked (used to build paths\",\n \"actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood =\",\n \"the Dockerfile :param str docker_url: the docker registry VIP accessible from the mesos\",\n \"\\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT']\",\n \"= False else: ret = True except: ret = False return ret def\",\n \"so get the matched position elif type(actual) is list: matchedPosition = -1 for\",\n \"try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\")\",\n \"structure. Could not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood = False\",\n \"files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return\",\n \"------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) #\",\n \"correct docker registry URL associated with the datacenter this script is being run\",\n \"if type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual,\",\n \"predefined structure. Could not proceed with deployment.\\\") # clean up unzipped stuff and\",\n \"the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from\",\n \"value , join all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for\",\n \"the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the\",\n \":param str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str\",\n \"pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag:\",\n \"not None: ret = False else: ret = True except: ret = False\",\n \"in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-')\",\n \"unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo,\",\n \"we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path,\",\n \"proceed with deployment.\\\") # out put report value , join all the messages\",\n \"for the docker registry functional user @return the latest image tag \\\"\\\"\\\" tag_list_url\",\n \"not be unzipped. Could not proceed with deployment.\\\") # out put report value\",\n \"dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\"\",\n \"Generic unzip function for extracting zip files @param zip_file the zip file to\",\n \"to open a JSON file @param jsonFile path to the JSON file @return\",\n \"file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does\",\n \"actual is a list then find out if they match anywhere, if so\",\n \"Function to check if files/directories match. They must have the same name and\",\n \"x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile,\",\n \"Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed\",\n \"find in the bundle @param currentPath the path currently being checked (used to\",\n \"addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood =\",\n \"used by Build Scripts A collection of common functions shared by each individual\",\n \"not meet predefined structure. Could not proceed with deployment.\\\") # clean up unzipped\",\n \"have the same name and must both be the same type. @param expectedElement\",\n \"and must both be the same type. @param expectedElement the expected element. May\",\n \"the global list of errorMessages @param errorMessage the error message to add \\\"\\\"\\\"\",\n \"latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for\",\n \"collection of common functions shared by each individual build scripts. \\\"\\\"\\\" def get(url,\",\n \"JSON file @return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file:\",\n \"timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # =======================\",\n \"not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded\",\n \"global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP\",\n \"directory to convert @return JSON representation of a directory \\\"\\\"\\\" d = {'name':\",\n \"an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined\",\n \"for extracting zip files @param zip_file the zip file to be extracted @param\",\n \"if an uploaded bundle follows IBM defined structure @param expected the JSON representation\",\n \"return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the global\",\n \"associated with the datacenter this script is being run on :param str dockerfile_parent_dir:\",\n \"[directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected,\",\n \"not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle),\",\n \"be anything then do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'],\",\n \"docker registry functional user @return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path\",\n \"with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest):\",\n \"subprocess import json import argparse import zipfile import shutil import requests import datetime\",\n \"image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker registry URL associated with\",\n \"on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the\",\n \"as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a\",\n \"contain files and/or directories. @param directory directory to convert @return JSON representation of\",\n \"registry functional user @return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path +\",\n \"recursively for error msg) @param isMatched (only used for recursive calls) @return True\",\n \"image_namespace: the name of the image :param str image_name: the name of the\",\n \"continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp\",\n \"bundle ZIP is located @param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\"\",\n \"the_zip_file.testzip() if badFile is not None: ret = False else: ret = True\",\n \"zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory,\",\n \"verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle follows IBM\",\n \"str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError:\",\n \"structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle name of\",\n \"# a matching file or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\":\",\n \"the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional\",\n \"path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function\",\n \"proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle\",\n \"300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client: \\\" +\",\n \"then verify it and its children if type(actual) is dict: isMatched = isMatched\",\n \"elif type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)): if\",\n \"verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood\",\n \"isMatched (only used for recursive calls) @return True if the list of actual\",\n \"extract the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's\",\n \"based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be\",\n \"zipFileName the zip file to be extracted @param directoryToUnzipTo the destination directory to\",\n \"log the error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log\",\n \"str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp\",\n \"= True if type(expected) is dict: if matches(expected,actual): # a matching file or\",\n \"badFile = the_zip_file.testzip() if badFile is not None: ret = False else: ret\",\n \"image :returns: None \\\"\\\"\\\" # Form the path for the Dockerfile based on\",\n \"tag in tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp =\",\n \"Common Functions used by Build Scripts A collection of common functions shared by\",\n \"registry @param pwd the password for the docker registry functional user @return the\",\n \"docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure\",\n \"False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip()\",\n \"the correct docker registry URL associated with the datacenter this script is being\",\n \"unicodedata # global list of error messages to keep track of all error\",\n \"@return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data =\",\n \"representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object\",\n \"isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match\",\n \"do further checking else: # a matching file or directory was not found\",\n \"element to find in the bundle @param currentPath the path currently being checked\",\n \"file to be tested @return True if the ZIP file is good. False\",\n \"currentPath) else : # if they don't match then log the error msg\",\n \"global array. @param expected the expected element @param currentPath the current path we\",\n \"and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure.\",\n \"@param zipDirectory where the bundle ZIP is located @param nameOfBundle name of the\",\n \"package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped\",\n \"be unzipped. Could not proceed with deployment.\\\") # out put report value ,\",\n \"be extracted @param to_dir the destination directory to extract the zip file to\",\n \"ret = True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\"\",\n \"& verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out if\",\n \"isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out\",\n \"# the children in a directory is a list composed of more files/directories\",\n \"error messages to the global list of errorMessages @param errorMessage the error message\",\n \"to add error messages to the global list of errorMessages @param errorMessage the\",\n \"FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance\",\n \"expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual list of objects\",\n \"representation of the IBM defined structure @param actual the JSON representation of the\",\n \"verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching file or\",\n \"= i break if matchedPosition != -1: # if they match then verify\",\n \"subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file is\",\n \"docker_url: the docker registry VIP accessible from the mesos slaves :param str image_namespace:\",\n \"package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could\",\n \"== actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\"\",\n \"environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker\",\n \"\\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for\",\n \"verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find out if they\",\n \"headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an\",\n \"= True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into\",\n \"tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip\",\n \"verifyBundleStructure. @param actual list of the actual files and directories in the bundle\",\n \"True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if\",\n \"definition, is defined to be the one with the highest version number (vX)\",\n \"logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to the global array. @param\",\n \"file is not valid: \\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\"\",\n \"match then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] ,\",\n \"suppressed verification once the docker cert location # is figured out and we\",\n \"the latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url,\",\n \"to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace,\",\n \"d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle\",\n \"uploaded bundle is: 1) a valid zip file 2) follows IBM defined structure\",\n \"dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") #\",\n \"latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag\",\n \"open a JSON file @param jsonFile path to the JSON file @return the\",\n \":param str image_tag_latest: the latest version tag of the base image :returns: None\",\n \"unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package\",\n \"dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" + image_name +\",\n \"the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------\",\n \"latest_tag = tag else: if latest_version > str_version: continue elif latest_version < str_version:\",\n \"if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function\",\n \"to verify if an uploaded bundle is: 1) a valid zip file 2)\",\n \"actual the JSON representation of the actual structure of the uploaded bundle @param\",\n \"current path we are on that has the missing file or directory \\\"\\\"\\\"\",\n \"IBM defined structure @param expected the JSON representation of the IBM defined structure\",\n \"currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched =\",\n \"= isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find\",\n \"addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not proceed with deployment.\\\") #\",\n \"matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match. They must have the\",\n \"command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close()\",\n \"up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while\",\n \"continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =\",\n \"by definition, is defined to be the one with the highest version number\",\n \"& verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching file\",\n \"ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if\",\n \"\\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write the FROM command string\",\n \"Function to test if a ZIP file is good or bad @param filePath\",\n \"def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] =\",\n \"bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip =\",\n \"list then find out if they match anywhere, if so get the matched\",\n \"contents of the directory can be anything then do no further checking else:\",\n \"checked (used to build paths recursively for error msg) @return True if structure\",\n \"if the list of actual objects contain the expected element \\\"\\\"\\\" # if\",\n \"file 2) follows IBM defined structure @param zipDirectory where the bundle ZIP is\",\n \"shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) #\",\n \"may contain files and/or directories. @param directory directory to convert @return JSON representation\",\n \"Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets\",\n \"the parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may\",\n \"one with the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param\",\n \"str dockerfile_parent_dir: path to the parent directory for the Dockerfile :param str docker_url:\",\n \"files and/or directories. @param directory directory to convert @return JSON representation of a\",\n \"A collection of common functions shared by each individual build scripts. \\\"\\\"\\\" def\",\n \"in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp,\",\n \"a valid zip file 2) follows IBM defined structure @param zipDirectory where the\",\n \"dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag\",\n \"request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if\",\n \"directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if\",\n \"while verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood = False else:\",\n \"latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:\",\n \"zipDirectory where the bundle ZIP is located @param nameOfBundle name of the bundle\",\n \"verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an image\",\n \"zip file 2) follows IBM defined structure @param zipDirectory where the bundle ZIP\",\n \"@param actualElement the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is\",\n \"Dockerfile based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\")\",\n \"Remove the suppressed verification once the docker cert location # is figured out\",\n \"anywhere, if so get the matched position elif type(actual) is list: matchedPosition =\",\n \"false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\"\",\n \"path to the JSON file @return the loaded JSON file \\\"\\\"\\\" try: with\",\n \"the zip file to be extracted @param to_dir the destination directory to extract\",\n \"if zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo =\",\n \"of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] =\",\n \"number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param\",\n \"2) follows IBM defined structure @param zipDirectory where the bundle ZIP is located\",\n \"+ \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories\",\n \"path for the Dockerfile based on the parent of the caller script dockerfile_path\",\n \"to be extracted @param directoryToUnzipTo the destination directory to extract the zip file\",\n \"directoryToUnzipTo the destination directory to extract the zip file to \\\"\\\"\\\" # update\",\n \"not proceed with deployment.\\\") # clean up unzipped stuff and package structure Json\",\n \"operator import unicodedata # global list of error messages to keep track of\",\n \"latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag =\",\n \"recursively for error msg) @return True if structure of the uploaded bundle follows\",\n \"the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip\",\n \"zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile\",\n \"Functions used by Build Scripts A collection of common functions shared by each\",\n \"Function to verify if an actual list of objects contains an expected element.\",\n \"str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue\",\n \"json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\"\",\n \"error messages to the global array. @param expected the expected element @param currentPath\",\n \"return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an\",\n \"+ image_name + \\\":\\\" + image_tag_latest # Write the FROM command string to\",\n \"\\\"\\\"\\\" Function to verify if an actual list of objects contains an expected\",\n \"list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition =\",\n \"the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret =\",\n \"expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to\",\n \"verify if an uploaded bundle follows IBM defined structure @param expected the JSON\",\n \"\\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification\",\n \"contains an expected element. Helper method to verifyBundleStructure. @param actual list of the\",\n \"to extract the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if\",\n \"expected the JSON representation of the IBM defined structure @param actual the JSON\",\n \"always a dict, it may contain children if type=directory. A directory is composed\",\n \"the JSON representation of the IBM defined structure @param actual the JSON representation\",\n \"the IBM defined structure @param actual the JSON representation of the actual structure\",\n \"stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle\",\n \"{'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\"\",\n \"\\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to\",\n \"not None and expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual,\",\n \"=============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param jsonFile path\",\n \"range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: #\",\n \"create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker\",\n \"= the_zip_file.testzip() if badFile is not None: ret = False else: ret =\",\n \"specified JSON file is not valid: \\\" + jsonFile) raise return data def\",\n \"if an actual list of objects contains an expected element. Helper method to\",\n \"same type. @param expectedElement the expected element. May be defined by regular expression\",\n \"base image :returns: None \\\"\\\"\\\" # Form the path for the Dockerfile based\",\n \"True if the ZIP file is good. False otherwise. \\\"\\\"\\\" ret = True\",\n \"== \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k in\",\n \"path @param usr the functional username for the docker registry @param pwd the\",\n \"is list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath,\",\n \"to_dir the destination directory to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file,\",\n \"docker registry path @param usr the functional username for the docker registry @param\",\n \"errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file\",\n \"Python module requests @param url the url of the REST call @param usr\",\n \"the docker registry @param pwd the password for the docker registry functional user\",\n \"once the docker cert location # is figured out and we specify it\",\n \"os import subprocess import json import argparse import zipfile import shutil import requests\",\n \"from the mesos slaves :param str image_namespace: the name of the image :param\",\n \"Function to verify if an uploaded bundle follows IBM defined structure @param expected\",\n \"@param currentPath the current path we are on that has the missing file\",\n \"it and its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual,\",\n \"TEMP: Remove the suppressed verification once the docker cert location # is figured\",\n \"of the directory can be anything then do no further checking else: isMatched\",\n \"\\\"\\\"\\\" Function to open a JSON file @param jsonFile path to the JSON\",\n \"error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used by Build\",\n \"tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one with\",\n \"element @param currentPath the current path we are on that has the missing\",\n \"else: if latest_version > str_version: continue elif latest_version < str_version: latest_version = str_version\",\n \"isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a matching\",\n \"the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName,\",\n \"pwd): \\\"\\\"\\\" Retrieve the latest version of an image based on its tags:\",\n \"try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file\",\n \"paths recursively for error msg) @param isMatched (only used for recursive calls) @return\",\n \"matching file or directory was found if expected['type'] == 'directory': currentPath = currentPath\",\n \"tested @return True if the ZIP file is good. False otherwise. \\\"\\\"\\\" ret\",\n \"else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to\",\n \"bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not proceed\",\n \"expected the expected element @param currentPath the current path we are on that\",\n \"latest version tag of the base image :returns: None \\\"\\\"\\\" # Form the\",\n \"expected element. May be defined by regular expression @param actualElement the actual element\",\n \"@param url the url of the REST call @param usr the functional username\",\n \"missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement,\",\n \"file is good or bad @param filePath the zip file to be tested\",\n \"regular expression @param actualElement the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'],\",\n \"match anywhere, if so get the matched position elif type(actual) is list: matchedPosition\",\n \"\\\"\\\"\\\" Function to verify if an uploaded bundle follows IBM defined structure @param\",\n \"bundle structure. Could not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood =\",\n \"verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds\",\n \"= True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function\",\n \"set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300'\",\n \"actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched & True #\",\n \"image_name: the name of the image :param str image_tag_latest: the latest version tag\",\n \"file @param jsonFile path to the JSON file @return the loaded JSON file\",\n \"the zip file to be extracted @param directoryToUnzipTo the destination directory to extract\",\n \"structure of the uploaded bundle @param currentPath the path currently being checked (used\",\n \"children if type=directory. A directory is composed of a list and may contain\",\n \"the path currently being checked (used to build paths recursively for error msg)\",\n \"the expected element. May be defined by regular expression @param actualElement the actual\",\n \"@param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...')\",\n \"raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given\",\n \"if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return false if needed\",\n \"parent directory for the Dockerfile :param str docker_url: the docker registry VIP accessible\",\n \"is composed of a list and may contain files and/or directories. @param directory\",\n \"def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param jsonFile path to\",\n \"+ \\\" seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function\",\n \"verify if an actual list of objects contains an expected element. Helper method\",\n \"actual objects contain the expected element \\\"\\\"\\\" # if actual is a dict\",\n \"import json import argparse import zipfile import shutil import requests import datetime import\",\n \"dict, it may contain children if type=directory. A directory is composed of a\",\n \"a list and may contain files and/or directories. @param directory directory to convert\",\n \"dict then verify it and its children if type(actual) is dict: isMatched =\",\n \"defined structure @param zipDirectory where the bundle ZIP is located @param nameOfBundle name\",\n \"\\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] ==\",\n \"functional username for the docker registry @param pwd the password for the docker\",\n \"Function to verify if an uploaded bundle is: 1) a valid zip file\",\n \"actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle follows IBM defined\",\n \"valid zip file 2) follows IBM defined structure @param zipDirectory where the bundle\",\n \"of the image :param str image_name: the name of the image :param str\",\n \"if actual is a dict then verify it and its children if type(actual)\",\n \"not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is\",\n \"global list of error messages to keep track of all error msgs errorMessages\",\n \"the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile =\",\n \"contain children if type=directory. A directory is composed of a list and may\",\n \"for error msg) @param isMatched (only used for recursive calls) @return True if\",\n \"\\\"\\\"\\\" Function to add error messages to the global list of errorMessages @param\",\n \"@param isMatched (only used for recursive calls) @return True if the list of\",\n \"unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files @param zip_file the\",\n \"in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def\",\n \"track of all error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions\",\n \"os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the\",\n \"if files/directories match. They must have the same name and must both be\",\n \"check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the\",\n \"can be anything then do no further checking else: isMatched = isMatched &\",\n \"addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match. They\",\n \"files @param zip_file the zip file to be extracted @param to_dir the destination\",\n \"'-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp\",\n \"if they don't match then log the error msg and return false if\",\n \"ZIP file is good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file =\",\n \"needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to\",\n \"Could not proceed with deployment.\\\") # clean up unzipped stuff and package structure\",\n \"JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The\",\n \"nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure\",\n \"Function to adds error messages to the global array. @param expected the expected\",\n \"the bundle @param expectedElement the expected element to find in the bundle @param\",\n \"working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo,\",\n \"username for the docker registry @param pwd the password for the docker registry\",\n \"the docker cert location # is figured out and we specify it in\",\n \"get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an image based on\",\n \"registry VIP accessible from the mesos slaves :param str image_namespace: the name of\",\n \"the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file\",\n \"# verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory,\",\n \"children in a directory is a list composed of more files/directories d['children'] =\",\n \"actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret = True return ret\",\n \"openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file\",\n \"parent object is always a dict, it may contain children if type=directory. A\",\n \"\\\"\\\"\\\" Function to verify if an uploaded bundle is: 1) a valid zip\",\n \"data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\" +\",\n \"('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try:\",\n \"verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle is: 1) a\",\n \"if matchedPosition != -1: # if they match then verify their children too\",\n \"messages to the global list of errorMessages @param errorMessage the error message to\",\n \"# Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace\",\n \"or directory was found if expected['type'] == 'directory': currentPath = currentPath + actual['name']\",\n \"verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't match then log\",\n \"[] \\\"\\\"\\\" Collection of Common Functions used by Build Scripts A collection of\",\n \"slaves :param str image_namespace: the name of the image :param str image_name: the\",\n \"requests @param url the url of the REST call @param usr the functional\",\n \"Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment\",\n \"isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath):\",\n \"# copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if\",\n \"usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests @param url\",\n \"ZIP file recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo\",\n \"else : # if they don't match then log the error msg and\",\n \"position elif type(actual) is list: matchedPosition = -1 for i in range(0, len(actual)):\",\n \"\\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url +\",\n \"+ image_tag_latest # Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) #\",\n \"in a directory is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x))\",\n \"matched position elif type(actual) is list: matchedPosition = -1 for i in range(0,\",\n \"structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy\",\n \"{ 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker\",\n \"into JSON form. The parent object is always a dict, it may contain\",\n \"unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file\",\n \"using the correct docker registry URL associated with the datacenter this script is\",\n \"actualElement): \\\"\\\"\\\" Function to check if files/directories match. They must have the same\",\n \"defined structure @param expected the JSON representation of the IBM defined structure @param\",\n \"May be defined by regular expression @param actualElement the actual element \\\"\\\"\\\" ret\",\n \"@param usr the functional username for the docker registry @param pwd the password\",\n \"extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close()\",\n \"directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given directory into JSON form.\",\n \"on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create\",\n \"IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict:\",\n \"the actual files and directories in the bundle @param expectedElement the expected element\",\n \"expected element @param currentPath the current path we are on that has the\",\n \"@param directory directory to convert @return JSON representation of a directory \\\"\\\"\\\" d\",\n \"or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False\",\n \"d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify\",\n \"latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp <\",\n \"os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo,\",\n \"else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not\",\n \"addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could not proceed with deployment.\\\")\",\n \"ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory,\",\n \"except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood\",\n \"True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new\",\n \"is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd),\",\n \"auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version\",\n \"type. @param expectedElement the expected element. May be defined by regular expression @param\",\n \"'300' print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\")\",\n \"# check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert\",\n \"for error msg) @return True if structure of the uploaded bundle follows IBM\",\n \"the ZIP file is good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file\",\n \"for the docker registry functional user @return a JSON response \\\"\\\"\\\" headers =\",\n \"\\\" + docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest\",\n \":returns: None \\\"\\\"\\\" # Form the path for the Dockerfile based on the\",\n \"False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded\",\n \"the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\"\",\n \"file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url,\",\n \"structure. Could not proceed with deployment.\\\") # clean up unzipped stuff and package\",\n \"the destination directory to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\")\",\n \"type(expected) is dict: if matches(expected,actual): # a matching file or directory was found\",\n \"is list: matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition\",\n \"Could not proceed with deployment.\\\") # out put report value , join all\",\n \"to unzip a ZIP file recursively @param zipFileName the zip file to be\",\n \"proceed with deployment.\\\") # clean up unzipped stuff and package structure Json -------------------------------------------------\",\n \"return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle\",\n \"defined to be the one with the highest version number (vX) and the\",\n \"if the contents of the directory can be anything then do no further\",\n \"> str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp\",\n \"of the IBM defined structure @param actual the JSON representation of the actual\",\n \"@param registry_path docker registry path @param usr the functional username for the docker\",\n \"d = {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type']\",\n \"image_name + \\\":\\\" + image_tag_latest # Write the FROM command string to the\",\n \"latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr,\",\n \"# is figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr,\",\n \"bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param\",\n \"bundle does not meet predefined structure. Could not proceed with deployment.\\\") # clean\",\n \"str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag\",\n \"the Dockerfile based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir,\",\n \"if type(expected) is dict: if matches(expected,actual): # a matching file or directory was\",\n \"registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag\",\n \"# log error msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement,\",\n \"good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile =\",\n \"= isMatched & True # if the contents of the directory can be\",\n \"the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file)\",\n \"Helper method to verifyBundleStructure. @param actual list of the actual files and directories\",\n \"GET requests using external Python module requests @param url the url of the\",\n \"directory can be anything then do no further checking else: isMatched = isMatched\",\n \"isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the global list\",\n \"string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def\",\n \"Function to unzip a ZIP file recursively @param zipFileName the zip file to\",\n \"may contain children if type=directory. A directory is composed of a list and\",\n \"type=directory. A directory is composed of a list and may contain files and/or\",\n \"image :param str image_tag_latest: the latest version tag of the base image :returns:\",\n \"if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def\",\n \"checked (used to build paths recursively for error msg) @param isMatched (only used\",\n \"a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object is dict\",\n \"bundle @param expectedElement the expected element to find in the bundle @param currentPath\",\n \"str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try:\",\n \"must both be the same type. @param expectedElement the expected element. May be\",\n \"they don't match then log the error msg and return false if expectedElement['fail-if-not-found']\",\n \"good or bad @param filePath the zip file to be tested @return True\",\n \"its children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) #\",\n \": # if they don't match then log the error msg and return\",\n \"+\\\" is missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return\",\n \"file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path:\",\n \"of the actual files and directories in the bundle @param expectedElement the expected\",\n \"for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x )\",\n \"for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return\",\n \"tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag\",\n \"if so get the matched position elif type(actual) is list: matchedPosition = -1\",\n \"zipfile import shutil import requests import datetime import re import operator import unicodedata\",\n \"error messages to keep track of all error msgs errorMessages = [] \\\"\\\"\\\"\",\n \"def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files @param zip_file\",\n \"Build Scripts A collection of common functions shared by each individual build scripts.\",\n \"the parent directory for the Dockerfile :param str docker_url: the docker registry VIP\",\n \"nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo\",\n \"and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the\",\n \"not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp =\",\n \", join all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x\",\n \"deployment.\\\") # out put report value , join all the messages together print\",\n \"actualElement the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not\",\n \"directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively @param zipFileName the zip\",\n \"= {'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type'] =\",\n \"otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if\",\n \"the actual structure of the uploaded bundle @param currentPath the path currently being\",\n \"The parent object is always a dict, it may contain children if type=directory.\",\n \"add error messages to the global list of errorMessages @param errorMessage the error\",\n \"'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the docker cert\",\n \"for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition\",\n \"< str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else: if\",\n \"to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir)\",\n \"external Python module requests @param url the url of the REST call @param\",\n \"highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry\",\n \"= datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version\",\n \"the contents of the directory can be anything then do no further checking\",\n \"found if expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if\",\n \"password for the docker registry functional user @return the latest image tag \\\"\\\"\\\"\",\n \"of Common Functions used by Build Scripts A collection of common functions shared\",\n \"directory directory to convert @return JSON representation of a directory \\\"\\\"\\\" d =\",\n \"directory is composed of a list and may contain files and/or directories. @param\",\n \"files/directories match. They must have the same name and must both be the\",\n \"str docker_url: the docker registry VIP accessible from the mesos slaves :param str\",\n \"is good or bad @param filePath the zip file to be tested @return\",\n \"if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo,\",\n \"dt_timestamp latest_tag = tag else: if latest_version > str_version: continue elif latest_version <\",\n \"the list of actual objects contain the expected element \\\"\\\"\\\" # if actual\",\n \"def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an image based\",\n \"to the JSON file @return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile)\",\n \"the error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error\",\n \"False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages\",\n \"to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName)\",\n \"list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched)\",\n \"tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp\",\n \"currentPath): \\\"\\\"\\\" Function to adds error messages to the global array. @param expected\",\n \"file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo)\",\n \"os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure =\",\n \"str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version > str_version: continue\",\n \"its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the one\",\n \"URL associated with the datacenter this script is being run on :param str\",\n \"list and may contain files and/or directories. @param directory directory to convert @return\",\n \"try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret\",\n \"bundle could not be unzipped. Could not proceed with deployment.\\\") # out put\",\n \"\\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" + currentPath\",\n \"registry_path docker registry path @param usr the functional username for the docker registry\",\n \"version of an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition,\",\n \"currentPath the current path we are on that has the missing file or\",\n \", currentPath) else : # if they don't match then log the error\",\n \"logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to\",\n \"<filename>scripts/common_lib/build_lib.py #!/usr/bin/env python3.6 import os import subprocess import json import argparse import zipfile\",\n \"bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected)\",\n \"@param expectedElement the expected element to find in the bundle @param currentPath the\",\n \"= False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error\",\n \"functional user @return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list'\",\n \"copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not\",\n \"x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath):\",\n \"@param expectedElement the expected element. May be defined by regular expression @param actualElement\",\n \"currentPath) # if actual is a list then find out if they match\",\n \"+ image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write the\",\n \"open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not\",\n \"messages to keep track of all error msgs errorMessages = [] \\\"\\\"\\\" Collection\",\n \"False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret =\",\n \"list of actual objects contain the expected element \\\"\\\"\\\" # if actual is\",\n \"tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag: continue\",\n \"caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path,\",\n \"tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version\",\n \"based on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") #\",\n \"recursively @param zipFileName the zip file to be extracted @param directoryToUnzipTo the destination\",\n \"list of objects contains an expected element. Helper method to verifyBundleStructure. @param actual\",\n \"bundle is: 1) a valid zip file 2) follows IBM defined structure @param\",\n \"if expected['children'] == \\\"_any\\\": isMatched = isMatched & True # if the contents\",\n \"They must have the same name and must both be the same type.\",\n \"instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\"\",\n \"os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0]))\",\n \"user @return a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } #\",\n \"JSON representation of the actual structure of the uploaded bundle @param currentPath the\",\n \"bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo)\",\n \"# Form the path for the Dockerfile based on the parent of the\",\n \"paths recursively for error msg) @return True if structure of the uploaded bundle\",\n \"i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition !=\",\n \"name and must both be the same type. @param expectedElement the expected element.\",\n \"Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace +\",\n \"os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return\",\n \"the REST call @param usr the functional username for the docker registry @param\",\n \"out put report value , join all the messages together print (\\\"report=[\\\" +\",\n \"of the REST call @param usr the functional username for the docker registry\",\n \"structure @param actual the JSON representation of the actual structure of the uploaded\",\n \"the uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True\",\n \"user @return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request\",\n \"= isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a\",\n \"is good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile\",\n \"print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip):\",\n \"seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open\"\n]","isConversational":false}}},{"rowIdx":3489,"cells":{"ngram":{"kind":"list like","value":["robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1.,","depth image :param depth_scale: depth scale that trans raw data to mm :return:","with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into","(int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640,","= None br_pxl = None for corner in corners: if corner[0] + corner[1]","center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img","dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to","cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth,","int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res =","max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the","project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d","opts import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory","cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param =","0., 1.00000]]) # default transformation from the camera to aruco tag default_M_CL =","KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def","Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False):","image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1,","np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522],","10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] +","-sys.maxsize tl_pxl = None br_pxl = None for corner in corners: if corner[0]","+= 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C","rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel","= project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix)","distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL","640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2),","division from __future__ import print_function import _init_paths import os import json import cv2","# initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt,","1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165,","cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners):","0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1.","reverse=True) # select the top 1 grasp prediction within the workspace res =","__future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths","the image into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"]","cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\",","= dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt)","nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth =","== 0: return [0, 0, 0, 0] # sort by the confidence score","kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL,","np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0.,","opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector","= aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing","within the workspace res = None for kp_pr in kps_pr: f_w, f_h =","0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range","kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1]","np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:,","cv2 import cv2.aruco as aruco import numpy as np import sys import rospy","visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init,","the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into the","return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL","/ 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm,","lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0),","46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL,","Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb","4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0,","numpy as np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg","= np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of","M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center,","camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689,","pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0],",":param M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix","kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and","of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): #","255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if","from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) #","cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])),","__future__ import division from __future__ import print_function import _init_paths import os import json","break if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0]","= np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at mean","base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the","480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None:",":param depth_scale: depth scale that trans raw data to mm :return: q_B: 3d","kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0]","channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub],","x, y = pxl[:] return w/12. <= x <= 11*w/12 and h/12. <=",":] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom","256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input","(int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map,","pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if","Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image :return: None","2: orientation = -np.pi - orientation else: orientation = -orientation # compute the","first frame, it may contain nothing if ids_CL is None: return default_M_CL, None","rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize max =","position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL,","print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res","aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for","> max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get","# replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) #","0, 0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x:","RGB image :param depth_data: raw depth image :return: None \"\"\" try: cv_rgb =","(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255),","import os import json import cv2 import cv2.aruco as aruco import numpy as","range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth","type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param","= project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] -","draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] -","default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293,","visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250)","# by counter-clock wise nei_range = 1 while depth == 0: for delta_x","find the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl","to convert ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT","= cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and","+ corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])]","dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw","markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create()","import message_filters import torch from external.nms import soft_nms from opts import opts from","dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base to aruco","depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items():","0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) #","parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the","frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected","(int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) /","corners) # replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth)","for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. / 512.","np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2],","the depth of the detected pixel is 0, check the depth of its","y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr =","[ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) #","_init_paths import os import json import cv2 import cv2.aruco as aruco import numpy",":return: q_B: 3d coordinate of pixel with respect to base frame ''' depth","+ corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max","0, 0, 0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda","delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] +","aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image = pre_process(img_wo_at,","pre_process(img_wo_at, depth) # pass the image into the network ret = detector.run(inp_image[:, :,","q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return","category_id, preds in net_output.items(): if len(preds) == 0: continue for pred in preds:","'__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type","depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub,","if corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl =","0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]])","no detection if len(kps_pr) == 0: return [0, 0, 0, 0] # sort","None for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. /","cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth","(%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0,","min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for","corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max =","coordinate of pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]]","= M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0]","# select the top 1 grasp prediction within the workspace res = None","M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if","pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0:",":param depth_data: raw depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\")","5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1]","print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x","inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def","depth == 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in","= 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1]","img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the","scale that trans raw data to mm :return: q_B: 3d coordinate of pixel","1000).astype(np.uint8) # get the current transformation from the camera to aruco tag M_CL,","import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from","import numpy as np import sys import rospy from std_msgs.msg import Bool from","cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL","= 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL,","= (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1])","in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei","opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector =","V1 :param rgb_data: RGB image :param depth_data: raw depth image :return: None \"\"\"","- orientation elif orientation < -np.pi / 2: orientation = -np.pi - orientation","0., 0., 0., 1. ]] ) # camera intrinsic matrix of Realsense D435","= opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task]","= np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth)","for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1]","get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10,","by depth info :param pixel: x, y :param M_CL: trans from camera to","aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0.,","= aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) #","datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base","sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg","import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from","detector_factory # transformation from the robot base to aruco tag M_BL = np.array([[1.,","Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL =","corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it","the top 1 grasp prediction within the workspace res = None for kp_pr","import soft_nms from opts import opts from logger import Logger from utils.utils import","depth of the detected pixel is 0, check the depth of its neighbors","* depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L","orientation = np.pi - orientation elif orientation < -np.pi / 2: orientation =","= np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :]","in corners: if corner[0] + corner[1] < min: min = corner[0] + corner[1]","depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256,","0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2)","int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm,","cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub","np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\",","if visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y))","cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) #","mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return","pixel is 0, check the depth of its neighbors # by counter-clock wise","M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h):","q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image =","CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return","kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return","from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory #","matrix :param depth_image: depth image :param depth_scale: depth scale that trans raw data","to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of","cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual',","== 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range,","np.pi / 2: orientation = np.pi - orientation elif orientation < -np.pi /","''' project 2d pixel on the image to 3d by depth info :param","0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data):","aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from","/ 2: orientation = -np.pi - orientation else: orientation = -orientation # compute","rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain","Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters","aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters)","the detected pixel is 0, check the depth of its neighbors # by","= np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495,","w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output,","detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL,","0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img,","in net_output.items(): if len(preds) == 0: continue for pred in preds: kps =","import cv2 import cv2.aruco as aruco import numpy as np import sys import","= cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep,","project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to","M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the","depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0:","depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth,","[center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node","depth_data: raw depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth","-0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt","w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): '''","1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image","0: return [0, 0, 0, 0] # sort by the confidence score kps_pr","cv2.aruco as aruco import numpy as np import sys import rospy from std_msgs.msg","0, check the depth of its neighbors # by counter-clock wise nei_range =","-np.pi - orientation else: orientation = -orientation # compute the open width dist","kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL,","corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max:","= cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr =","(256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth","M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds)","isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None: return [0,","draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0,","of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0,","to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352],","dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) #","depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation =","0: break if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot(","= cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr","img, False) # remove aruco tag from input image to avoid mis-detection if","ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix,","depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d =","intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that trans raw","kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0]","e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <=","min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1]","M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d by","gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation","+ delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth","depth != 0: break if depth != 0: break nei_range += 1 pxl","0), 2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]),",") # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064],","wise nei_range = 1 while depth == 0: for delta_x in range(-nei_range, nei_range","+ 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1],","get_M_CL_info(gray, img, False) # remove aruco tag from input image to avoid mis-detection","0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0.,","512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm","= [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth","__future__ import print_function import _init_paths import os import json import cv2 import cv2.aruco","= False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) #","left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0,","= np.pi - orientation elif orientation < -np.pi / 2: orientation = -np.pi","x[-1], reverse=True) # select the top 1 grasp prediction within the workspace res","Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False #","def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x <=","br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image,","orientation elif orientation < -np.pi / 2: orientation = -np.pi - orientation else:","jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3]","import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import",":, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and","image :param depth_scale: depth scale that trans raw data to mm :return: q_B:","kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi","/ 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img =","range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0]","# draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2,","f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w),","0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] )","nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth]))","255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation,","+ 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w,","325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs","range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out","data to mm :return: q_B: 3d coordinate of pixel with respect to base","= cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the","import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import","# motor 7 is clockwise if orientation > np.pi / 2: orientation =","- kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep =","2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img =","to 3d by depth info :param pixel: x, y :param M_CL: trans from","640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] *","= aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first","rgb_data: RGB image :param depth_data: raw depth image :return: None \"\"\" try: cv_rgb","parameters=parameters) # for the first frame, it may contain nothing if ids_CL is",":param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale","at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL)","input image to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img,","Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters","<= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = []","corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners min =","[0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the","lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] -","may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL","from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import","depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L =","f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if","int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d","return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input from","= np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep","from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge,","h): x, y = pxl[:] return w/12. <= x <= 11*w/12 and h/12.","for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) /","cameraMatrix): ''' project 2d pixel on the image to 3d by depth info","kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input from Kinect V1 :param","+ (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.))","import print_function import _init_paths import os import json import cv2 import cv2.aruco as","to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param","get the current transformation from the camera to aruco tag M_CL, corners =","M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1.,","= np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img =","= depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0: break","= (depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera to","max = -sys.maxsize tl_pxl = None br_pxl = None for corner in corners:","net_output.items(): if len(preds) == 0: continue for pred in preds: kps = pred[:4]","= np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse()","480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm =","orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2])","cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])),","int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0,","dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if","the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403,","corners: if corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl","and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1]","nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0]","the image to 3d by depth info :param pixel: x, y :param M_CL:","is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the","0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset]","0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw","img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw *","dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3,","int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1]","corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None","Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector","located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL,","trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image:","if the depth of the detected pixel is 0, check the depth of","from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth","= opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of","of pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] #","kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0,","= Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def","\"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb,","= aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints =","np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1])","= pre_process(img_wo_at, depth) # pass the image into the network ret = detector.run(inp_image[:,","def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners","np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation","check the depth of its neighbors # by counter-clock wise nei_range = 1","11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL,","that trans raw data to mm :return: q_B: 3d coordinate of pixel with","0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2],","width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle","(int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix)","Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res =","preds in net_output.items(): if len(preds) == 0: continue for pred in preds: kps","< min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0]","y = pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y","corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]),","cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr =","cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center","# for the first frame, it may contain nothing if ids_CL is None:","by counter-clock wise nei_range = 1 while depth == 0: for delta_x in","return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on","cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr","-orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw","= rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return","timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub","default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594,","markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3]","= corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel","cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] =","the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the","neighbors # by counter-clock wise nei_range = 1 while depth == 0: for","isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res","and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix,","if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel","pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C)","raw RGB and depth input from Kinect V1 :param rgb_data: RGB image :param","1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0])","soft_nms from opts import opts from logger import Logger from utils.utils import AverageMeter","rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual',","= cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8)",":param depth_image: depth image :param depth_scale: depth scale that trans raw data to","(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255),","!= 0: break if depth != 0: break nei_range += 1 pxl =","> np.pi / 2: orientation = np.pi - orientation elif orientation < -np.pi","is None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm","as np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import","-np.pi / 2: orientation = -np.pi - orientation else: orientation = -orientation #","np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset","tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768,","br_pxl = None for corner in corners: if corner[0] + corner[1] < min:","+ delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0:","= img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from","f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res","from __future__ import division from __future__ import print_function import _init_paths import os import","left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.),","5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0]","pixel[0]] # if the depth of the detected pixel is 0, check the","cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth =","def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image","if orientation > np.pi / 2: orientation = np.pi - orientation elif orientation","- 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for","10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w","-0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0.,","it may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL,","right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] +","and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl","[0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs =","pixel on the image to 3d by depth info :param pixel: x, y","the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove","2d pixel on the image to 3d by depth info :param pixel: x,","= 1 while depth == 0: for delta_x in range(-nei_range, nei_range + 1):","!= 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1]","''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel","Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms","import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from","the network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret,","= tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco","return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w),","# parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters","def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id,","np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray =",",mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def","(depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera to aruco","kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm","0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w),","orientation < -np.pi / 2: orientation = -np.pi - orientation else: orientation =","]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0,","rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1]","kps[3], score]) # no detection if len(kps_pr) == 0: return [0, 0, 0,","depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) #","%(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :]","from the robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000],","grasp prediction within the workspace res = None for kp_pr in kps_pr: f_w,","aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray,","(int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2,","0., 0., 1.00000]]) # default transformation from the camera to aruco tag default_M_CL","(int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) /","not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth","isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x <= 11*w/12","the first frame, it may contain nothing if ids_CL is None: return default_M_CL,","raw depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth =","(kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img","3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: #","(kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep","transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178],","CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image)","CvBridgeError import message_filters import torch from external.nms import soft_nms from opts import opts","trans raw data to mm :return: q_B: 3d coordinate of pixel with respect","* f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480):","import absolute_import from __future__ import division from __future__ import print_function import _init_paths import","default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL =","in kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm =",":return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr","0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] *","dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb)","distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt =","2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize:","= KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e)","x: x[-1], reverse=True) # select the top 1 grasp prediction within the workspace","utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation","the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :]","rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm,","depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data,","image :param depth_data: raw depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data,","print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs,","respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth","img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from the","compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for","255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img","M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is located",":, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data,","= pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y <=","top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None","f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480)","False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe","cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) #","from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms","w, h): x, y = pxl[:] return w/12. <= x <= 11*w/12 and","1.00000]]) # default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498,","2: orientation = np.pi - orientation elif orientation < -np.pi / 2: orientation","f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break","aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints","= -np.pi - orientation else: orientation = -orientation # compute the open width","/ 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] +","Bridge to convert ROS Image type to OpenCV Image type cv_bridge = CvBridge()","rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts","None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL)","ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw,","f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center","nei_range = 1 while depth == 0: for delta_x in range(-nei_range, nei_range +","(int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm,","to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera","image into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori","project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0])",":param rgb_data: RGB image :param depth_data: raw depth image :return: None \"\"\" try:","- kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi / 2:","calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth",":3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0,","if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] *","image to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners)","nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0],","# if the depth of the detected pixel is 0, check the depth","aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3,","(int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2,","(0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1],","= pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) ==","tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is","0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt =","0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera","w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color","from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot","[int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] + corner[1]","pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img):","kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi / 2: orientation","import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge","nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if","5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep,","key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1]","- kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.),","camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image","= rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093","and depth input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw","False) # remove aruco tag from input image to avoid mis-detection if corners","= [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] +","convert ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT =","corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] #","1 while depth == 0: for delta_x in range(-nei_range, nei_range + 1): for","avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace","1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]]","= depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is 0,","aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame,","= corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] >","img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix):","os import json import cv2 import cv2.aruco as aruco import numpy as np","inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw","# sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True)","from opts import opts from logger import Logger from utils.utils import AverageMeter from","aruco import numpy as np import sys import rospy from std_msgs.msg import Bool","blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the","depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w,","# Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init,","q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image","%d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :,","[] for category_id, preds in net_output.items(): if len(preds) == 0: continue for pred","if len(preds) == 0: continue for pred in preds: kps = pred[:4] score","(int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] *","Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]])","tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth","0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from","corners = get_M_CL_info(gray, img, False) # remove aruco tag from input image to","pass the image into the network ret = detector.run(inp_image[:, :, :]) ret =","aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image,","0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera","== 0: continue for pred in preds: kps = pred[:4] score = pred[-1]","0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1],","by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select","M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1]","= cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]),","606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847,","Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result","= get_M_CL_info(gray, img, False) # remove aruco tag from input image to avoid","in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return","sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction within","= sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction","f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h))","<= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr","mm :return: q_B: 3d coordinate of pixel with respect to base frame '''","key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img =","-0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic","ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except","opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher","0, 1]) if visualize: # print('aruco is located at mean position (%d, %d)'","matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0,","right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl =","sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) #","- 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1]","rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to OpenCV Image type cv_bridge","= img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] -","1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera to","# initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to","0, 0, 1]) if visualize: # print('aruco is located at mean position (%d,","2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])),","and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2)","2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist]","from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from","cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from","of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet","M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor","base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0.,","Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector","- 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] +","3d coordinate of pixel with respect to base frame ''' depth = depth_image[pixel[1],","= np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B","for the first frame, it may contain nothing if ids_CL is None: return","# print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix,","# get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] -","45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46):","kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d","import detector_factory # transformation from the robot base to aruco tag M_BL =","aruco tag from input image to avoid mis-detection if corners is not None:","distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize","x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map,","# no detection if len(kps_pr) == 0: return [0, 0, 0, 0] #","= (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3]","* f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640,","len(kps_pr) == 0: return [0, 0, 0, 0] # sort by the confidence","rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image","import json import cv2 import cv2.aruco as aruco import numpy as np import","kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL,","else: orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] -","(kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) /","cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': #","std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from","inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input from Kinect","img_out = rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize","on the image to 3d by depth info :param pixel: x, y :param","depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is","delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break","= [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] -","sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for corner in","sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch","replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for","46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :]","M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param","* f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h))","current transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img,","as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12.","<filename>src/static_grasp_kt.py from __future__ import absolute_import from __future__ import division from __future__ import print_function","cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8)","cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL,","5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img,","orientation = -np.pi - orientation else: orientation = -orientation # compute the open","= -sys.maxsize tl_pxl = None br_pxl = None for corner in corners: if","br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0]","kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center key-points","cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw","kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img,","orientation else: orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2]","project 2d pixel on the image to 3d by depth info :param pixel:","right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255),","CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo,","rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image,","detection if len(kps_pr) == 0: return [0, 0, 0, 0] # sort by","distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out","tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL =","depth scale that trans raw data to mm :return: q_B: 3d coordinate of","img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl,","ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as","rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in","7 is clockwise if orientation > np.pi / 2: orientation = np.pi -","= depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\"","center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") #","cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None)","message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback)","tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy()","depth_image: depth image :param depth_scale: depth scale that trans raw data to mm","cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy()","1 grasp prediction within the workspace res = None for kp_pr in kps_pr:","break if res is None: return [0, 0, 0, 0] f_w, f_h =","= (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm,","depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is 0, check","int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] -","# find the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize","depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get","0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm","cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y","Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import","corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left","is clockwise if orientation > np.pi / 2: orientation = np.pi - orientation","center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0]","pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image,","ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to OpenCV Image","for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0]","= -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) #","depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save","# aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL,","center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\")","0., 0., 1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix","- orientation else: orientation = -orientation # compute the open width dist =",":] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project","depth of its neighbors # by counter-clock wise nei_range = 1 while depth","def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) #","rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize","= [] for category_id, preds in net_output.items(): if len(preds) == 0: continue for","corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with","initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to OpenCV","-0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]]","arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0])","# compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow","640, 480): res = kp_pr break if res is None: return [0, 0,","= rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize max","0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic matrix of","+ (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.))","int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0,","= None for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480.","GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt)","# camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0,","depth_scale: depth scale that trans raw data to mm :return: q_B: 3d coordinate","KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds","absolute_import from __future__ import division from __future__ import print_function import _init_paths import os","import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import",":, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix)","torch from external.nms import soft_nms from opts import opts from logger import Logger","center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d =","rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out =","- 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def","visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds) == 0:","pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL =","480): res = kp_pr break if res is None: return [0, 0, 0,","(0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2)","[0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h))","kps_pr = [] for category_id, preds in net_output.items(): if len(preds) == 0: continue","cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__':","project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation","is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL,","cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion","cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1],","the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl =","to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000],","# Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to","\"\"\" Save raw RGB and depth input from Kinect V1 :param rgb_data: RGB","def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input from Kinect V1","M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7","is 0, check the depth of its neighbors # by counter-clock wise nei_range","inp_image = pre_process(img_wo_at, depth) # pass the image into the network ret =","\"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr)","external.nms import soft_nms from opts import opts from logger import Logger from utils.utils","= np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if","camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and","import division from __future__ import print_function import _init_paths import os import json import","y :param M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic","corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color =","kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return [0, 0,","M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map,","if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if","+ 1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] +","depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L)","cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that","= None for corner in corners: if corner[0] + corner[1] < min: min",":] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in","__name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS","/ 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] +","- kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img =","= dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1])","= np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img","node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to OpenCV Image type","0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435","import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base to","aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing if ids_CL","tag from input image to avoid mis-detection if corners is not None: img_wo_at","corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]),","1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C =","OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration","kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi /","int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0,","0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL,","tl_pxl = None br_pxl = None for corner in corners: if corner[0] +","CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from opts import","score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr)",":] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at","rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img","delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth !=","get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL","= aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4))","prediction within the workspace res = None for kp_pr in kps_pr: f_w, f_h","from __future__ import absolute_import from __future__ import division from __future__ import print_function import","int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return","1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default","orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge","Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray,","network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img,","max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement","the depth of its neighbors # by counter-clock wise nei_range = 1 while","def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image =","preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) #","x, y :param M_CL: trans from camera to aruco tag :param cameraMatrix: camera","rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle","if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL,","in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45,","delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1):","q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :,","clockwise if orientation > np.pi / 2: orientation = np.pi - orientation elif","kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return [0,","CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import","img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image","= cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] =","= 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center =","# transformation from the robot base to aruco tag M_BL = np.array([[1., 0.,","value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in","\"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32)","(kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0),","tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46):","std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError","result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL","= (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1])","from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image :return:","f_w, f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] *","np.pi - orientation elif orientation < -np.pi / 2: orientation = -np.pi -","import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from opts","= pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection","+ corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])]","= (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d =","RGB and depth input from Kinect V1 :param rgb_data: RGB image :param depth_data:","visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__","= detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL,","D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) #","= ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError","* 1000).astype(np.uint8) # get the current transformation from the camera to aruco tag","pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h","to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image)","tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0]","= cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB","transformation from the robot base to aruco tag M_BL = np.array([[1., 0., 0.,",":]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori)","loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e:","return w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12 def","# draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0]","key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction within the","image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\")","break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth,","motor 7 is clockwise if orientation > np.pi / 2: orientation = np.pi","type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get","+ (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0])","detectors.detector_factory import detector_factory # transformation from the robot base to aruco tag M_BL","(int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img)","res = kp_pr break if res is None: return [0, 0, 0, 0]","<= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL,","-0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic matrix","except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:]","pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if","pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img,","(0, 0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img =","= cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]),","480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2))","tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0.,","counter-clock wise nei_range = 1 while depth == 0: for delta_x in range(-nei_range,","= detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result',","= project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix)","camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco","[ 0., 0., 0., 1. ]] ) # camera intrinsic matrix of Realsense","the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle","rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL","for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2],","print_function import _init_paths import os import json import cv2 import cv2.aruco as aruco","rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] -","of its neighbors # by counter-clock wise nei_range = 1 while depth ==","-0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera to aruco","remove aruco tag from input image to avoid mis-detection if corners is not","replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass","- kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation >","br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h,","h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] -","open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and","M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from input image","+ 46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL,","= message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1)","depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0: break nei_range","corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if","min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] +","cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and","Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback) rospy.spin()","info :param pixel: x, y :param M_CL: trans from camera to aruco tag","the current transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray,",":param pixel: x, y :param M_CL: trans from camera to aruco tag :param","to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag","for corner in corners: if corner[0] + corner[1] < min: min = corner[0]","Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\",","[0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) #","orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise","the workspace res = None for kp_pr in kps_pr: f_w, f_h = 640.","np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense","= np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray","rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,","/ 2: orientation = np.pi - orientation elif orientation < -np.pi / 2:","11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for","- kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2)","in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y]","None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL,","cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ ==","image to 3d by depth info :param pixel: x, y :param M_CL: trans","# distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) #","0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm =","Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory","aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners min","M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] -","aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [","confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top","M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d by depth","camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that trans","rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\",","1]) if visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x","is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs)","img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45,","= sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for corner","depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B =","+ corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color","2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0,","[0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134,","rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,","-0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [","message_filters import torch from external.nms import soft_nms from opts import opts from logger","None for corner in corners: if corner[0] + corner[1] < min: min =","its neighbors # by counter-clock wise nei_range = 1 while depth == 0:","# pass the image into the network ret = detector.run(inp_image[:, :, :]) ret","* depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B","M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0,","- kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0]","kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w),","# remove aruco tag from input image to avoid mis-detection if corners is","None br_pxl = None for corner in corners: if corner[0] + corner[1] <","from input image to avoid mis-detection if corners is not None: img_wo_at =","== '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image","intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934],","= rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub =","if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert","# cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw =","depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d","cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current","3d by depth info :param pixel: x, y :param M_CL: trans from camera","elif orientation < -np.pi / 2: orientation = -np.pi - orientation else: orientation","parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters =","parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel","int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL,","/ 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h))","0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0.,","depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into the network","camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420,","score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1","0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0.,","[0., 0., 0., 1.00000]]) # default transformation from the camera to aruco tag","opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory","Save raw RGB and depth input from Kinect V1 :param rgb_data: RGB image","to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) #","0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation","np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at mean position","markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() #","[-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0.,","AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the","0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102,","= aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image =","pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y =","logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory","cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0,","= np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1],","<= x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img,","channel inp_image = pre_process(img_wo_at, depth) # pass the image into the network ret","workspace res = None for kp_pr in kps_pr: f_w, f_h = 640. /","queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)","import torch from external.nms import soft_nms from opts import opts from logger import","= rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d","nei[0]] if depth != 0: break if depth != 0: break nei_range +=","pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12","depth info :param pixel: x, y :param M_CL: trans from camera to aruco","512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] *","raw data to mm :return: q_B: 3d coordinate of pixel with respect to","if depth != 0: break if depth != 0: break nei_range += 1","if corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl =","return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img","depth input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth","import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import","continue for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1],","kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img","cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds) ==","initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset)","* f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr","2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle,","Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector =","np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray","from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912,","as aruco import numpy as np import sys import rospy from std_msgs.msg import","contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL =","M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x,","for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] =","mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue","score]) # no detection if len(kps_pr) == 0: return [0, 0, 0, 0]","and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None: return","[int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10,","corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value","pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if","= (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL,","kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] +","0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2)","None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr =","[0, 0, 0, 0] # sort by the confidence score kps_pr = sorted(kps_pr,","q_B: 3d coordinate of pixel with respect to base frame ''' depth =","with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the","inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256))","detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray,","json import cv2 import cv2.aruco as aruco import numpy as np import sys","corner in corners: if corner[0] + corner[1] < min: min = corner[0] +","= (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm,","aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing if","depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera","= cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE)","np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy()","len(preds) == 0: continue for pred in preds: kps = pred[:4] score =","try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8)","in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score])","0., 1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix =","dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points","from external.nms import soft_nms from opts import opts from logger import Logger from","to mm :return: q_B: 3d coordinate of pixel with respect to base frame","from detectors.detector_factory import detector_factory # transformation from the robot base to aruco tag","return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find","np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def","if len(kps_pr) == 0: return [0, 0, 0, 0] # sort by the","opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception","1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x,","orientation > np.pi / 2: orientation = np.pi - orientation elif orientation <","# Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub =","if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if","perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters","from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo","for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range +","pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3],","640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is","= kp_pr break if res is None: return [0, 0, 0, 0] f_w,","2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0,","kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp","+ (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0,","select the top 1 grasp prediction within the workspace res = None for","frame, it may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL,","h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True):","M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] =","if res is None: return [0, 0, 0, 0] f_w, f_h = 640./512.,","image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL =","= CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info',","detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10)","None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth channel","cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img","D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt","while depth == 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y","top 1 grasp prediction within the workspace res = None for kp_pr in","cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is","kp_pr break if res is None: return [0, 0, 0, 0] f_w, f_h","int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map,","# Bridge to convert ROS Image type to OpenCV Image type cv_bridge =","depth) # pass the image into the network ret = detector.run(inp_image[:, :, :])","input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image","None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm =","return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros","# get the current transformation from the camera to aruco tag M_CL, corners","channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image","2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0],","import cv2.aruco as aruco import numpy as np import sys import rospy from","-0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset =","aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale:","range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei =","into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori =","ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False","tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from input","np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep =","import _init_paths import os import json import cv2 import cv2.aruco as aruco import","res is None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512.","= np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450],","/ 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2]","the robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0.,","transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False)","of the detected pixel is 0, check the depth of its neighbors #","int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl","M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize:","[pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth !=","# default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369,","np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] =","240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283,","ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may","and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts =","for category_id, preds in net_output.items(): if len(preds) == 0: continue for pred in","detected pixel is 0, check the depth of its neighbors # by counter-clock","res = None for kp_pr in kps_pr: f_w, f_h = 640. / 512.,","kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no","_objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4,","45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def project(pixel,","from __future__ import print_function import _init_paths import os import json import cv2 import","return [0, 0, 0, 0] # sort by the confidence score kps_pr =","rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL","depth_data): \"\"\" Save raw RGB and depth input from Kinect V1 :param rgb_data:","< -np.pi / 2: orientation = -np.pi - orientation else: orientation = -orientation","0: continue for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0],","M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3,","pixel: x, y :param M_CL: trans from camera to aruco tag :param cameraMatrix:"],"string":"[\n \"robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1.,\",\n \"depth image :param depth_scale: depth scale that trans raw data to mm :return:\",\n \"with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into\",\n \"(int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640,\",\n \"= None br_pxl = None for corner in corners: if corner[0] + corner[1]\",\n \"center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img\",\n \"dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to\",\n \"cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth,\",\n \"int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res =\",\n \"max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the\",\n \"project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d\",\n \"opts import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory\",\n \"cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param =\",\n \"0., 1.00000]]) # default transformation from the camera to aruco tag default_M_CL =\",\n \"KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def\",\n \"Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False):\",\n \"image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1,\",\n \"np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522],\",\n \"10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] +\",\n \"-sys.maxsize tl_pxl = None br_pxl = None for corner in corners: if corner[0]\",\n \"+= 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C\",\n \"rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel\",\n \"= project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL\",\n \"640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2),\",\n \"division from __future__ import print_function import _init_paths import os import json import cv2\",\n \"# initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt,\",\n \"1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165,\",\n \"cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners):\",\n \"0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1.\",\n \"reverse=True) # select the top 1 grasp prediction within the workspace res =\",\n \"__future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths\",\n \"the image into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"]\",\n \"cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\",\",\n \"= dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt)\",\n \"nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth =\",\n \"== 0: return [0, 0, 0, 0] # sort by the confidence score\",\n \"kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL,\",\n \"np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0.,\",\n \"opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector\",\n \"= aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing\",\n \"within the workspace res = None for kp_pr in kps_pr: f_w, f_h =\",\n \"0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range\",\n \"kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1]\",\n \"np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:,\",\n \"cv2 import cv2.aruco as aruco import numpy as np import sys import rospy\",\n \"visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init,\",\n \"the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into the\",\n \"return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL\",\n \"/ 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm,\",\n \"lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0),\",\n \"46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL,\",\n \"Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb\",\n \"4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0,\",\n \"numpy as np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg\",\n \"= np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of\",\n \"M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center,\",\n \"camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689,\",\n \"pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0],\",\n \":param M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix\",\n \"kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and\",\n \"of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): #\",\n \"255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if\",\n \"from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) #\",\n \"cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])),\",\n \"__future__ import division from __future__ import print_function import _init_paths import os import json\",\n \"break if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0]\",\n \"= np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at mean\",\n \"base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the\",\n \"480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None:\",\n \":param depth_scale: depth scale that trans raw data to mm :return: q_B: 3d\",\n \"kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0]\",\n \"channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub],\",\n \"x, y = pxl[:] return w/12. <= x <= 11*w/12 and h/12. <=\",\n \":] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom\",\n \"256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input\",\n \"(int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map,\",\n \"pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if\",\n \"Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image :return: None\",\n \"2: orientation = -np.pi - orientation else: orientation = -orientation # compute the\",\n \"first frame, it may contain nothing if ids_CL is None: return default_M_CL, None\",\n \"rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize max =\",\n \"position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL,\",\n \"print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res\",\n \"aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for\",\n \"> max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get\",\n \"# replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) #\",\n \"0, 0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x:\",\n \"RGB image :param depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb =\",\n \"(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255),\",\n \"import os import json import cv2 import cv2.aruco as aruco import numpy as\",\n \"range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth\",\n \"type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param\",\n \"= project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] -\",\n \"draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] -\",\n \"default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293,\",\n \"visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250)\",\n \"# by counter-clock wise nei_range = 1 while depth == 0: for delta_x\",\n \"find the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl\",\n \"to convert ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT\",\n \"= cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and\",\n \"+ corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])]\",\n \"dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw\",\n \"markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create()\",\n \"import message_filters import torch from external.nms import soft_nms from opts import opts from\",\n \"dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base to aruco\",\n \"depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items():\",\n \"0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) #\",\n \"parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the\",\n \"frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected\",\n \"(int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) /\",\n \"corners) # replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth)\",\n \"for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. / 512.\",\n \"np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2],\",\n \"the depth of the detected pixel is 0, check the depth of its\",\n \"y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr =\",\n \"[ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) #\",\n \"_init_paths import os import json import cv2 import cv2.aruco as aruco import numpy\",\n \":return: q_B: 3d coordinate of pixel with respect to base frame ''' depth\",\n \"+ corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max\",\n \"0, 0, 0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda\",\n \"delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] +\",\n \"aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image = pre_process(img_wo_at,\",\n \"pre_process(img_wo_at, depth) # pass the image into the network ret = detector.run(inp_image[:, :,\",\n \"q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return\",\n \"category_id, preds in net_output.items(): if len(preds) == 0: continue for pred in preds:\",\n \"'__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type\",\n \"depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub,\",\n \"if corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl =\",\n \"0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]])\",\n \"no detection if len(kps_pr) == 0: return [0, 0, 0, 0] # sort\",\n \"None for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. /\",\n \"cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth\",\n \"(%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0,\",\n \"min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for\",\n \"corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max =\",\n \"coordinate of pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]]\",\n \"= M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0]\",\n \"# select the top 1 grasp prediction within the workspace res = None\",\n \"M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if\",\n \"pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0:\",\n \":param depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\")\",\n \"5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1]\",\n \"print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x\",\n \"inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def\",\n \"depth == 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in\",\n \"= 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1]\",\n \"img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the\",\n \"scale that trans raw data to mm :return: q_B: 3d coordinate of pixel\",\n \"1000).astype(np.uint8) # get the current transformation from the camera to aruco tag M_CL,\",\n \"import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from\",\n \"import numpy as np import sys import rospy from std_msgs.msg import Bool from\",\n \"cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL\",\n \"= 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL,\",\n \"= (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1])\",\n \"in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei\",\n \"opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector =\",\n \"V1 :param rgb_data: RGB image :param depth_data: raw depth image :return: None \\\"\\\"\\\"\",\n \"- orientation elif orientation < -np.pi / 2: orientation = -np.pi - orientation\",\n \"0., 0., 0., 1. ]] ) # camera intrinsic matrix of Realsense D435\",\n \"= opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task]\",\n \"= np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth)\",\n \"for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1]\",\n \"get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10,\",\n \"by depth info :param pixel: x, y :param M_CL: trans from camera to\",\n \"aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0.,\",\n \"= aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) #\",\n \"datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base\",\n \"sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg\",\n \"import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from\",\n \"detector_factory # transformation from the robot base to aruco tag M_BL = np.array([[1.,\",\n \"Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL =\",\n \"corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it\",\n \"the top 1 grasp prediction within the workspace res = None for kp_pr\",\n \"import soft_nms from opts import opts from logger import Logger from utils.utils import\",\n \"depth of the detected pixel is 0, check the depth of its neighbors\",\n \"* depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L\",\n \"orientation = np.pi - orientation elif orientation < -np.pi / 2: orientation =\",\n \"= np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :]\",\n \"in corners: if corner[0] + corner[1] < min: min = corner[0] + corner[1]\",\n \"depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256,\",\n \"0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2)\",\n \"int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm,\",\n \"cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub\",\n \"np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\",\",\n \"if visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y))\",\n \"cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) #\",\n \"mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return\",\n \"pixel is 0, check the depth of its neighbors # by counter-clock wise\",\n \"M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h):\",\n \"q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image =\",\n \"CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return\",\n \"kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return\",\n \"from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory #\",\n \"matrix :param depth_image: depth image :param depth_scale: depth scale that trans raw data\",\n \"to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth of\",\n \"cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual',\",\n \"== 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range,\",\n \"np.pi / 2: orientation = np.pi - orientation elif orientation < -np.pi /\",\n \"''' project 2d pixel on the image to 3d by depth info :param\",\n \"0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data):\",\n \"aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from\",\n \"/ 2: orientation = -np.pi - orientation else: orientation = -orientation # compute\",\n \"rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain\",\n \"Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters\",\n \"aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters)\",\n \"the detected pixel is 0, check the depth of its neighbors # by\",\n \"= np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495,\",\n \"w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output,\",\n \"detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL,\",\n \"0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img,\",\n \"in net_output.items(): if len(preds) == 0: continue for pred in preds: kps =\",\n \"import cv2 import cv2.aruco as aruco import numpy as np import sys import\",\n \"= cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep,\",\n \"project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to\",\n \"M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the\",\n \"depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0:\",\n \"depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth,\",\n \"[center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node\",\n \"depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth\",\n \"-0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt\",\n \"w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): '''\",\n \"1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image\",\n \"0: return [0, 0, 0, 0] # sort by the confidence score kps_pr\",\n \"cv2.aruco as aruco import numpy as np import sys import rospy from std_msgs.msg\",\n \"0, check the depth of its neighbors # by counter-clock wise nei_range =\",\n \"-np.pi - orientation else: orientation = -orientation # compute the open width dist\",\n \"kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL,\",\n \"corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max:\",\n \"= cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr =\",\n \"(256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth\",\n \"M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds)\",\n \"isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None: return [0,\",\n \"draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0,\",\n \"of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0,\",\n \"to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352],\",\n \"dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) #\",\n \"depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation =\",\n \"0: break if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot(\",\n \"= cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr\",\n \"img, False) # remove aruco tag from input image to avoid mis-detection if\",\n \"ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix,\",\n \"depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d =\",\n \"intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that trans raw\",\n \"kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0]\",\n \"e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <=\",\n \"min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1]\",\n \"M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d by\",\n \"gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation\",\n \"+ delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth\",\n \"depth != 0: break if depth != 0: break nei_range += 1 pxl\",\n \"0), 2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]),\",\n \") # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064],\",\n \"wise nei_range = 1 while depth == 0: for delta_x in range(-nei_range, nei_range\",\n \"+ 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1],\",\n \"get_M_CL_info(gray, img, False) # remove aruco tag from input image to avoid mis-detection\",\n \"0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0.,\",\n \"512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm\",\n \"= [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth\",\n \"__future__ import print_function import _init_paths import os import json import cv2 import cv2.aruco\",\n \"= False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) #\",\n \"left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0,\",\n \"= np.pi - orientation elif orientation < -np.pi / 2: orientation = -np.pi\",\n \"x[-1], reverse=True) # select the top 1 grasp prediction within the workspace res\",\n \"Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False #\",\n \"def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x <=\",\n \"br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image,\",\n \"orientation elif orientation < -np.pi / 2: orientation = -np.pi - orientation else:\",\n \"jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3]\",\n \"import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import\",\n \":, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and\",\n \"image :param depth_scale: depth scale that trans raw data to mm :return: q_B:\",\n \"kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi\",\n \"/ 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img =\",\n \"range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0]\",\n \"# draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2,\",\n \"f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w),\",\n \"0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] )\",\n \"nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth]))\",\n \"255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation,\",\n \"+ 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w,\",\n \"325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs\",\n \"range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out\",\n \"data to mm :return: q_B: 3d coordinate of pixel with respect to base\",\n \"= cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the\",\n \"import opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import\",\n \"# motor 7 is clockwise if orientation > np.pi / 2: orientation =\",\n \"- kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep =\",\n \"2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img =\",\n \"to 3d by depth info :param pixel: x, y :param M_CL: trans from\",\n \"640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] *\",\n \"= aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first\",\n \"rgb_data: RGB image :param depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb\",\n \"parameters=parameters) # for the first frame, it may contain nothing if ids_CL is\",\n \":param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale\",\n \"at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL)\",\n \"input image to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img,\",\n \"Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration parameters\",\n \"<= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = []\",\n \"corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners min =\",\n \"[0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the\",\n \"lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] -\",\n \"may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL\",\n \"from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import\",\n \"depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L =\",\n \"f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if\",\n \"int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d\",\n \"return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input from\",\n \"= np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep\",\n \"from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge,\",\n \"h): x, y = pxl[:] return w/12. <= x <= 11*w/12 and h/12.\",\n \"for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) /\",\n \"cameraMatrix): ''' project 2d pixel on the image to 3d by depth info\",\n \"kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input from Kinect V1 :param\",\n \"+ (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.))\",\n \"import print_function import _init_paths import os import json import cv2 import cv2.aruco as\",\n \"to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param\",\n \"get the current transformation from the camera to aruco tag M_CL, corners =\",\n \"M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1.,\",\n \"= np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img =\",\n \"= depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0: break\",\n \"= (depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera to\",\n \"max = -sys.maxsize tl_pxl = None br_pxl = None for corner in corners:\",\n \"net_output.items(): if len(preds) == 0: continue for pred in preds: kps = pred[:4]\",\n \"= np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse()\",\n \"480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm =\",\n \"orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2])\",\n \"cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])),\",\n \"int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0,\",\n \"dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if\",\n \"the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403,\",\n \"corners: if corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl\",\n \"and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1]\",\n \"nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0]\",\n \"the image to 3d by depth info :param pixel: x, y :param M_CL:\",\n \"is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the\",\n \"0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset]\",\n \"0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw\",\n \"img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw *\",\n \"dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3,\",\n \"int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1]\",\n \"corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None\",\n \"Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector\",\n \"located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL,\",\n \"trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image:\",\n \"if the depth of the detected pixel is 0, check the depth of\",\n \"from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth\",\n \"= opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of\",\n \"of pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] #\",\n \"kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0,\",\n \"= Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def\",\n \"\\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb,\",\n \"= aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints =\",\n \"np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1])\",\n \"= pre_process(img_wo_at, depth) # pass the image into the network ret = detector.run(inp_image[:,\",\n \"def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners\",\n \"np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation\",\n \"check the depth of its neighbors # by counter-clock wise nei_range = 1\",\n \"11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL,\",\n \"that trans raw data to mm :return: q_B: 3d coordinate of pixel with\",\n \"0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2],\",\n \"width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle\",\n \"(int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res =\",\n \"preds in net_output.items(): if len(preds) == 0: continue for pred in preds: kps\",\n \"< min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0]\",\n \"y = pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y\",\n \"corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]),\",\n \"cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr =\",\n \"cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center\",\n \"# for the first frame, it may contain nothing if ids_CL is None:\",\n \"by counter-clock wise nei_range = 1 while depth == 0: for delta_x in\",\n \"return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on\",\n \"cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr\",\n \"-orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw\",\n \"= rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return\",\n \"timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub\",\n \"default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594,\",\n \"markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3]\",\n \"= corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel\",\n \"cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] =\",\n \"the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the\",\n \"neighbors # by counter-clock wise nei_range = 1 while depth == 0: for\",\n \"isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res\",\n \"and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix,\",\n \"if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel\",\n \"pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C)\",\n \"raw RGB and depth input from Kinect V1 :param rgb_data: RGB image :param\",\n \"1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0])\",\n \"soft_nms from opts import opts from logger import Logger from utils.utils import AverageMeter\",\n \"rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual',\",\n \"= cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8)\",\n \":param depth_image: depth image :param depth_scale: depth scale that trans raw data to\",\n \"(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255),\",\n \"!= 0: break if depth != 0: break nei_range += 1 pxl =\",\n \"> np.pi / 2: orientation = np.pi - orientation elif orientation < -np.pi\",\n \"is None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm\",\n \"as np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import\",\n \"-np.pi / 2: orientation = -np.pi - orientation else: orientation = -orientation #\",\n \"np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset\",\n \"tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768,\",\n \"br_pxl = None for corner in corners: if corner[0] + corner[1] < min:\",\n \"+ delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0:\",\n \"= img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from\",\n \"f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res\",\n \"from __future__ import division from __future__ import print_function import _init_paths import os import\",\n \"left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.),\",\n \"5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0]\",\n \"pixel[0]] # if the depth of the detected pixel is 0, check the\",\n \"cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth =\",\n \"def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image\",\n \"if orientation > np.pi / 2: orientation = np.pi - orientation elif orientation\",\n \"- 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for\",\n \"10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w\",\n \"-0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0.,\",\n \"it may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL,\",\n \"right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] +\",\n \"and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl\",\n \"[0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs =\",\n \"pixel on the image to 3d by depth info :param pixel: x, y\",\n \"the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove\",\n \"2d pixel on the image to 3d by depth info :param pixel: x,\",\n \"= 1 while depth == 0: for delta_x in range(-nei_range, nei_range + 1):\",\n \"!= 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1]\",\n \"''' depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel\",\n \"Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms\",\n \"import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from\",\n \"the network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret,\",\n \"= tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco\",\n \"return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w),\",\n \"# parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters\",\n \"def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id,\",\n \"np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray =\",\n \",mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def\",\n \"(depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera to aruco\",\n \"kp_pr in kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm\",\n \"0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w),\",\n \"orientation < -np.pi / 2: orientation = -np.pi - orientation else: orientation =\",\n \"]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0,\",\n \"rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1]\",\n \"kps[3], score]) # no detection if len(kps_pr) == 0: return [0, 0, 0,\",\n \"depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) #\",\n \"%(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :]\",\n \"from the robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000],\",\n \"grasp prediction within the workspace res = None for kp_pr in kps_pr: f_w,\",\n \"aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray,\",\n \"(int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2,\",\n \"0., 0., 1.00000]]) # default transformation from the camera to aruco tag default_M_CL\",\n \"(int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) /\",\n \"not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth\",\n \"isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <= x <= 11*w/12\",\n \"the first frame, it may contain nothing if ids_CL is None: return default_M_CL,\",\n \"raw depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth =\",\n \"(kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img\",\n \"3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: #\",\n \"(kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep\",\n \"transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178],\",\n \"CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image)\",\n \"CvBridgeError import message_filters import torch from external.nms import soft_nms from opts import opts\",\n \"trans raw data to mm :return: q_B: 3d coordinate of pixel with respect\",\n \"* f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480):\",\n \"import absolute_import from __future__ import division from __future__ import print_function import _init_paths import\",\n \"default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL =\",\n \"in kps_pr: f_w, f_h = 640. / 512., 480. / 512. kp_lm =\",\n \":return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr\",\n \"0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] *\",\n \"dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb)\",\n \"distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt =\",\n \"2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize:\",\n \"= KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e)\",\n \"x: x[-1], reverse=True) # select the top 1 grasp prediction within the workspace\",\n \"utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation\",\n \"the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :]\",\n \"rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm,\",\n \"depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data,\",\n \"image :param depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data,\",\n \"print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs,\",\n \"respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth\",\n \"img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from the\",\n \"compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for\",\n \"255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img\",\n \"M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is located\",\n \":, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data,\",\n \"= pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y <=\",\n \"top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None\",\n \"f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480)\",\n \"False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe\",\n \"cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) #\",\n \"from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms\",\n \"w, h): x, y = pxl[:] return w/12. <= x <= 11*w/12 and\",\n \"1.00000]]) # default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498,\",\n \"2: orientation = np.pi - orientation elif orientation < -np.pi / 2: orientation\",\n \"f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break\",\n \"aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints\",\n \"= -np.pi - orientation else: orientation = -orientation # compute the open width\",\n \"/ 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] +\",\n \"Bridge to convert ROS Image type to OpenCV Image type cv_bridge = CvBridge()\",\n \"rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts\",\n \"None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL)\",\n \"ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw,\",\n \"f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center\",\n \"nei_range = 1 while depth == 0: for delta_x in range(-nei_range, nei_range +\",\n \"(int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm,\",\n \"to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera\",\n \"image into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori\",\n \"project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0])\",\n \":param rgb_data: RGB image :param depth_data: raw depth image :return: None \\\"\\\"\\\" try:\",\n \"- kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi / 2:\",\n \"calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth\",\n \":3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0,\",\n \"if depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] *\",\n \"image to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners)\",\n \"nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0],\",\n \"# if the depth of the detected pixel is 0, check the depth\",\n \"aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3,\",\n \"(int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2,\",\n \"(0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1],\",\n \"= pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) ==\",\n \"tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is\",\n \"0.0]) # initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt =\",\n \"0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera\",\n \"w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color\",\n \"from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot\",\n \"[int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] + corner[1]\",\n \"pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img):\",\n \"kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi / 2: orientation\",\n \"import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge\",\n \"nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if\",\n \"5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep,\",\n \"key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1]\",\n \"- kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.),\",\n \"camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image\",\n \"= rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093\",\n \"and depth input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw\",\n \"False) # remove aruco tag from input image to avoid mis-detection if corners\",\n \"= [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] +\",\n \"convert ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT =\",\n \"corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] #\",\n \"1 while depth == 0: for delta_x in range(-nei_range, nei_range + 1): for\",\n \"avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace\",\n \"1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]]\",\n \"= depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is 0,\",\n \"aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame,\",\n \"= corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] >\",\n \"img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix):\",\n \"os import json import cv2 import cv2.aruco as aruco import numpy as np\",\n \"inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw\",\n \"# sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True)\",\n \"from opts import opts from logger import Logger from utils.utils import AverageMeter from\",\n \"aruco import numpy as np import sys import rospy from std_msgs.msg import Bool\",\n \"blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the\",\n \"depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w,\",\n \"# Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init,\",\n \"q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image\",\n \"%d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :,\",\n \"[] for category_id, preds in net_output.items(): if len(preds) == 0: continue for pred\",\n \"if len(preds) == 0: continue for pred in preds: kps = pred[:4] score\",\n \"(int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] *\",\n \"Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]])\",\n \"tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth\",\n \"0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from\",\n \"corners = get_M_CL_info(gray, img, False) # remove aruco tag from input image to\",\n \"pass the image into the network ret = detector.run(inp_image[:, :, :]) ret =\",\n \"aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image,\",\n \"0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera\",\n \"== 0: continue for pred in preds: kps = pred[:4] score = pred[-1]\",\n \"0] # sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1],\",\n \"by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select\",\n \"M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1]\",\n \"= cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]),\",\n \"606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847,\",\n \"Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result\",\n \"= get_M_CL_info(gray, img, False) # remove aruco tag from input image to avoid\",\n \"in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return\",\n \"sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction within\",\n \"= sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction\",\n \"f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h))\",\n \"<= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr\",\n \"mm :return: q_B: 3d coordinate of pixel with respect to base frame '''\",\n \"key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img =\",\n \"-0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic\",\n \"ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except\",\n \"opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher\",\n \"0, 1]) if visualize: # print('aruco is located at mean position (%d, %d)'\",\n \"matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0,\",\n \"right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl =\",\n \"sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) #\",\n \"- 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1]\",\n \"rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to OpenCV Image type cv_bridge\",\n \"= img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] -\",\n \"1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera to\",\n \"# initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to\",\n \"0, 0, 1]) if visualize: # print('aruco is located at mean position (%d,\",\n \"2) # draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])),\",\n \"and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2)\",\n \"2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist]\",\n \"from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from\",\n \"cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from\",\n \"of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet\",\n \"M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor\",\n \"base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0.,\",\n \"Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector\",\n \"- 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] +\",\n \"3d coordinate of pixel with respect to base frame ''' depth = depth_image[pixel[1],\",\n \"= np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B\",\n \"for the first frame, it may contain nothing if ids_CL is None: return\",\n \"# print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix,\",\n \"# get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] -\",\n \"45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46):\",\n \"kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d\",\n \"import detector_factory # transformation from the robot base to aruco tag M_BL =\",\n \"aruco tag from input image to avoid mis-detection if corners is not None:\",\n \"distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize\",\n \"x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map,\",\n \"# no detection if len(kps_pr) == 0: return [0, 0, 0, 0] #\",\n \"= (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3]\",\n \"* f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640,\",\n \"len(kps_pr) == 0: return [0, 0, 0, 0] # sort by the confidence\",\n \"rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image\",\n \"import json import cv2 import cv2.aruco as aruco import numpy as np import\",\n \"kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL,\",\n \"else: orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] -\",\n \"(kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) /\",\n \"cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': #\",\n \"std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from\",\n \"inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input from Kinect\",\n \"img_out = rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize\",\n \"on the image to 3d by depth info :param pixel: x, y :param\",\n \"depth = depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is\",\n \"delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break\",\n \"= [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] -\",\n \"sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for corner in\",\n \"sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch\",\n \"replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for\",\n \"46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :]\",\n \"M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param\",\n \"* f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h))\",\n \"current transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img,\",\n \"as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12.\",\n \"<filename>src/static_grasp_kt.py from __future__ import absolute_import from __future__ import division from __future__ import print_function\",\n \"cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8)\",\n \"cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL,\",\n \"5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img,\",\n \"orientation = -np.pi - orientation else: orientation = -orientation # compute the open\",\n \"= -sys.maxsize tl_pxl = None br_pxl = None for corner in corners: if\",\n \"br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0]\",\n \"kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center key-points\",\n \"cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw\",\n \"kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img,\",\n \"orientation else: orientation = -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2]\",\n \"project 2d pixel on the image to 3d by depth info :param pixel:\",\n \"right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255),\",\n \"CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo,\",\n \"rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image,\",\n \"detection if len(kps_pr) == 0: return [0, 0, 0, 0] # sort by\",\n \"distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out\",\n \"tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL =\",\n \"depth scale that trans raw data to mm :return: q_B: 3d coordinate of\",\n \"img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl,\",\n \"ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as\",\n \"rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in\",\n \"7 is clockwise if orientation > np.pi / 2: orientation = np.pi -\",\n \"= depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\"\",\n \"center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") #\",\n \"cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None)\",\n \"message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback)\",\n \"tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy()\",\n \"depth_image: depth image :param depth_scale: depth scale that trans raw data to mm\",\n \"cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy()\",\n \"1 grasp prediction within the workspace res = None for kp_pr in kps_pr:\",\n \"break if res is None: return [0, 0, 0, 0] f_w, f_h =\",\n \"= (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm,\",\n \"depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel is 0, check\",\n \"int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] -\",\n \"# find the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize\",\n \"depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get\",\n \"0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm\",\n \"cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y\",\n \"Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import\",\n \"corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left\",\n \"is clockwise if orientation > np.pi / 2: orientation = np.pi - orientation\",\n \"center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0]\",\n \"pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image,\",\n \"ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to OpenCV Image\",\n \"for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0]\",\n \"= -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) #\",\n \"depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save\",\n \"# aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL,\",\n \"center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\")\",\n \"0., 0., 1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix\",\n \"- orientation else: orientation = -orientation # compute the open width dist =\",\n \":] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project\",\n \"depth of its neighbors # by counter-clock wise nei_range = 1 while depth\",\n \"def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) #\",\n \"rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize\",\n \"= [] for category_id, preds in net_output.items(): if len(preds) == 0: continue for\",\n \"corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with\",\n \"initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to OpenCV\",\n \"-0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]]\",\n \"arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0])\",\n \"# compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow\",\n \"640, 480): res = kp_pr break if res is None: return [0, 0,\",\n \"= rgb_image.copy() # find the top-left and right-bottom corners min = sys.maxsize max\",\n \"0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic matrix of\",\n \"+ (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.))\",\n \"int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0,\",\n \"= None for kp_pr in kps_pr: f_w, f_h = 640. / 512., 480.\",\n \"GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt)\",\n \"# camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0,\",\n \"depth_scale: depth scale that trans raw data to mm :return: q_B: 3d coordinate\",\n \"KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds\",\n \"absolute_import from __future__ import division from __future__ import print_function import _init_paths import os\",\n \"import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import\",\n \":, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix)\",\n \"torch from external.nms import soft_nms from opts import opts from logger import Logger\",\n \"center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d =\",\n \"rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out =\",\n \"- 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def\",\n \"visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds) == 0:\",\n \"pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL =\",\n \"480): res = kp_pr break if res is None: return [0, 0, 0,\",\n \"(0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2)\",\n \"[0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h))\",\n \"kps_pr = [] for category_id, preds in net_output.items(): if len(preds) == 0: continue\",\n \"cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__':\",\n \"project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation\",\n \"is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL,\",\n \"cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion\",\n \"cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1],\",\n \"the top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl =\",\n \"to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000],\",\n \"# Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to\",\n \"\\\"\\\"\\\" Save raw RGB and depth input from Kinect V1 :param rgb_data: RGB\",\n \"def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input from Kinect V1\",\n \"M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7\",\n \"is 0, check the depth of its neighbors # by counter-clock wise nei_range\",\n \"inp_image = pre_process(img_wo_at, depth) # pass the image into the network ret =\",\n \"\\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr)\",\n \"external.nms import soft_nms from opts import opts from logger import Logger from utils.utils\",\n \"= np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if\",\n \"camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and\",\n \"import division from __future__ import print_function import _init_paths import os import json import\",\n \"y :param M_CL: trans from camera to aruco tag :param cameraMatrix: camera intrinsic\",\n \"corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color =\",\n \"kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return [0, 0,\",\n \"M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map,\",\n \"if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if\",\n \"+ 1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] +\",\n \"depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L)\",\n \"cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that\",\n \"= None for corner in corners: if corner[0] + corner[1] < min: min\",\n \":] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in\",\n \"__name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS\",\n \"/ 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] +\",\n \"- kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img =\",\n \"= dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1])\",\n \"= np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img\",\n \"node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to OpenCV Image type\",\n \"0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435\",\n \"import dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot base to\",\n \"aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing if ids_CL\",\n \"tag from input image to avoid mis-detection if corners is not None: img_wo_at\",\n \"corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]),\",\n \"1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C =\",\n \"OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration\",\n \"kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi /\",\n \"int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0,\",\n \"0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL,\",\n \"tl_pxl = None br_pxl = None for corner in corners: if corner[0] +\",\n \"CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from opts import\",\n \"score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr)\",\n \":] = np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at\",\n \"rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img\",\n \"delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break if depth !=\",\n \"get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL\",\n \"= aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4))\",\n \"prediction within the workspace res = None for kp_pr in kps_pr: f_w, f_h\",\n \"from __future__ import absolute_import from __future__ import division from __future__ import print_function import\",\n \"int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return\",\n \"1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default\",\n \"orientation, dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge\",\n \"Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray,\",\n \"network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img,\",\n \"max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement\",\n \"the depth of its neighbors # by counter-clock wise nei_range = 1 while\",\n \"def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image =\",\n \"preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) #\",\n \"x, y :param M_CL: trans from camera to aruco tag :param cameraMatrix: camera\",\n \"rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle\",\n \"if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL,\",\n \"in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45,\",\n \"delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1):\",\n \"q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :,\",\n \"clockwise if orientation > np.pi / 2: orientation = np.pi - orientation elif\",\n \"kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) == 0: return [0,\",\n \"CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms import\",\n \"img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image\",\n \"= cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] =\",\n \"= 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center =\",\n \"# transformation from the robot base to aruco tag M_BL = np.array([[1., 0.,\",\n \"value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in\",\n \"\\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32)\",\n \"(kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0),\",\n \"tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46):\",\n \"std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError\",\n \"result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL\",\n \"= (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1])\",\n \"from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image :return:\",\n \"f_w, f_h = 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] *\",\n \"np.pi - orientation elif orientation < -np.pi / 2: orientation = -np.pi -\",\n \"import CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms from opts\",\n \"= pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection\",\n \"+ corner[1] > max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])]\",\n \"= (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d =\",\n \"RGB and depth input from Kinect V1 :param rgb_data: RGB image :param depth_data:\",\n \"visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__\",\n \"= detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL,\",\n \"D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) #\",\n \"= ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError\",\n \"* 1000).astype(np.uint8) # get the current transformation from the camera to aruco tag\",\n \"pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h\",\n \"to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image)\",\n \"tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max = corner[0]\",\n \"= cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB\",\n \"transformation from the robot base to aruco tag M_BL = np.array([[1., 0., 0.,\",\n \":]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori)\",\n \"loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e:\",\n \"return w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12 def\",\n \"# draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0]\",\n \"key=lambda x: x[-1], reverse=True) # select the top 1 grasp prediction within the\",\n \"image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\")\",\n \"break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth,\",\n \"motor 7 is clockwise if orientation > np.pi / 2: orientation = np.pi\",\n \"type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get\",\n \"+ (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0])\",\n \"detectors.detector_factory import detector_factory # transformation from the robot base to aruco tag M_BL\",\n \"(int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img)\",\n \"res = kp_pr break if res is None: return [0, 0, 0, 0]\",\n \"<= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL,\",\n \"-0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) # camera intrinsic matrix\",\n \"except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:]\",\n \"pixel with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if\",\n \"pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img,\",\n \"(0, 0, 0), 2) # draw left-middle, right-middle and center key-points rgb_img =\",\n \"= cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]),\",\n \"480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2))\",\n \"tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0.,\",\n \"counter-clock wise nei_range = 1 while depth == 0: for delta_x in range(-nei_range,\",\n \"= detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result',\",\n \"= project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco\",\n \"[ 0., 0., 0., 1. ]] ) # camera intrinsic matrix of Realsense\",\n \"the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle\",\n \"rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL\",\n \"for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2],\",\n \"print_function import _init_paths import os import json import cv2 import cv2.aruco as aruco\",\n \"rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] -\",\n \"of its neighbors # by counter-clock wise nei_range = 1 while depth ==\",\n \"-0.0450], [0., 0., 0., 1.00000]]) # default transformation from the camera to aruco\",\n \"remove aruco tag from input image to avoid mis-detection if corners is not\",\n \"replace blue channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass\",\n \"- kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation >\",\n \"br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h,\",\n \"h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0] -\",\n \"open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and\",\n \"M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from input image\",\n \"+ 46): img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image, M_CL,\",\n \"= message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1)\",\n \"depth_image[nei[1], nei[0]] if depth != 0: break if depth != 0: break nei_range\",\n \"corner[1] < min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if\",\n \"min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] +\",\n \"cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and\",\n \"Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback) rospy.spin()\",\n \"info :param pixel: x, y :param M_CL: trans from camera to aruco tag\",\n \"the current transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray,\",\n \":param pixel: x, y :param M_CL: trans from camera to aruco tag :param\",\n \"to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag\",\n \"for corner in corners: if corner[0] + corner[1] < min: min = corner[0]\",\n \"Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\",\",\n \"[0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) #\",\n \"orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise\",\n \"the workspace res = None for kp_pr in kps_pr: f_w, f_h = 640.\",\n \"np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense\",\n \"= np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray\",\n \"rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,\",\n \"/ 2: orientation = np.pi - orientation elif orientation < -np.pi / 2:\",\n \"11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for\",\n \"- kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2)\",\n \"in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y]\",\n \"None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL,\",\n \"cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ ==\",\n \"image to 3d by depth info :param pixel: x, y :param M_CL: trans\",\n \"# distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) #\",\n \"0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm =\",\n \"Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory\",\n \"aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and right-bottom corners min\",\n \"M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] -\",\n \"aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [\",\n \"confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top\",\n \"M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d by depth\",\n \"camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale that trans\",\n \"rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\",\",\n \"1]) if visualize: # print('aruco is located at mean position (%d, %d)' %(mean_x\",\n \"is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs)\",\n \"img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45,\",\n \"= sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None for corner\",\n \"depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B =\",\n \"+ corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color\",\n \"2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0,\",\n \"[0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134,\",\n \"rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,\",\n \"-0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [\",\n \"message_filters import torch from external.nms import soft_nms from opts import opts from logger\",\n \"None for corner in corners: if corner[0] + corner[1] < min: min =\",\n \"its neighbors # by counter-clock wise nei_range = 1 while depth == 0:\",\n \"# pass the image into the network ret = detector.run(inp_image[:, :, :]) ret\",\n \"* depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B\",\n \"M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0,\",\n \"- kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0]\",\n \"kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w),\",\n \"# remove aruco tag from input image to avoid mis-detection if corners is\",\n \"None br_pxl = None for corner in corners: if corner[0] + corner[1] <\",\n \"from input image to avoid mis-detection if corners is not None: img_wo_at =\",\n \"== '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image\",\n \"intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934],\",\n \"= rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub =\",\n \"if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert\",\n \"# cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw =\",\n \"depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image to 3d\",\n \"cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current\",\n \"3d by depth info :param pixel: x, y :param M_CL: trans from camera\",\n \"elif orientation < -np.pi / 2: orientation = -np.pi - orientation else: orientation\",\n \"parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters =\",\n \"parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel\",\n \"int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL,\",\n \"/ 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h))\",\n \"0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0.,\",\n \"depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into the network\",\n \"camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420,\",\n \"score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1\",\n \"0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0.,\",\n \"[0., 0., 0., 1.00000]]) # default transformation from the camera to aruco tag\",\n \"opts from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory\",\n \"Save raw RGB and depth input from Kinect V1 :param rgb_data: RGB image\",\n \"to avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) #\",\n \"0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation\",\n \"np.array([0, 0, 0, 1]) if visualize: # print('aruco is located at mean position\",\n \"markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() #\",\n \"[-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0.,\",\n \"AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from the\",\n \"0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102,\",\n \"= aruco_tag_remove(img, corners) # replace blue channel with the depth channel inp_image =\",\n \"pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y =\",\n \"logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory\",\n \"cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0,\",\n \"= np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1],\",\n \"<= x <= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img,\",\n \"channel inp_image = pre_process(img_wo_at, depth) # pass the image into the network ret\",\n \"workspace res = None for kp_pr in kps_pr: f_w, f_h = 640. /\",\n \"queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)\",\n \"import torch from external.nms import soft_nms from opts import opts from logger import\",\n \"= rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d\",\n \"nei[0]] if depth != 0: break if depth != 0: break nei_range +=\",\n \"pxl[:] return w/12. <= x <= 11*w/12 and h/12. <= y <= 11*h/12\",\n \"depth info :param pixel: x, y :param M_CL: trans from camera to aruco\",\n \"512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] *\",\n \"raw data to mm :return: q_B: 3d coordinate of pixel with respect to\",\n \"if depth != 0: break if depth != 0: break nei_range += 1\",\n \"if corner[0] + corner[1] < min: min = corner[0] + corner[1] tl_pxl =\",\n \"return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img\",\n \"depth input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth\",\n \"import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import\",\n \"continue for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1],\",\n \"kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img\",\n \"cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if len(preds) ==\",\n \"initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset)\",\n \"* f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr\",\n \"2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle,\",\n \"Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector =\",\n \"np import sys import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray\",\n \"from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912,\",\n \"as aruco import numpy as np import sys import rospy from std_msgs.msg import\",\n \"contain nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL =\",\n \"M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x,\",\n \"for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] =\",\n \"mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue\",\n \"score]) # no detection if len(kps_pr) == 0: return [0, 0, 0, 0]\",\n \"and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is None: return\",\n \"[int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10,\",\n \"corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value\",\n \"pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if\",\n \"= (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL,\",\n \"kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] +\",\n \"0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2)\",\n \"None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr =\",\n \"[0, 0, 0, 0] # sort by the confidence score kps_pr = sorted(kps_pr,\",\n \"q_B: 3d coordinate of pixel with respect to base frame ''' depth =\",\n \"with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the\",\n \"inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256))\",\n \"detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray,\",\n \"json import cv2 import cv2.aruco as aruco import numpy as np import sys\",\n \"corner in corners: if corner[0] + corner[1] < min: min = corner[0] +\",\n \"= (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm,\",\n \"aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may contain nothing if\",\n \"depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation from the camera\",\n \"= cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE)\",\n \"np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy()\",\n \"len(preds) == 0: continue for pred in preds: kps = pred[:4] score =\",\n \"try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8)\",\n \"in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score])\",\n \"0., 1. ]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix =\",\n \"dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points\",\n \"from external.nms import soft_nms from opts import opts from logger import Logger from\",\n \"to mm :return: q_B: 3d coordinate of pixel with respect to base frame\",\n \"from detectors.detector_factory import detector_factory # transformation from the robot base to aruco tag\",\n \"return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find\",\n \"np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def\",\n \"if len(kps_pr) == 0: return [0, 0, 0, 0] # sort by the\",\n \"opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception\",\n \"1): for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x,\",\n \"orientation > np.pi / 2: orientation = np.pi - orientation elif orientation <\",\n \"# Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub =\",\n \"if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if\",\n \"perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters\",\n \"from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo\",\n \"for delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range +\",\n \"pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3],\",\n \"640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is\",\n \"= kp_pr break if res is None: return [0, 0, 0, 0] f_w,\",\n \"2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0,\",\n \"kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1 grasp\",\n \"+ (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0,\",\n \"select the top 1 grasp prediction within the workspace res = None for\",\n \"frame, it may contain nothing if ids_CL is None: return default_M_CL, None rvec_CL,\",\n \"h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True):\",\n \"M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] =\",\n \"if res is None: return [0, 0, 0, 0] f_w, f_h = 640./512.,\",\n \"image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL =\",\n \"= CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info',\",\n \"detector = Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10)\",\n \"None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the depth channel\",\n \"cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img\",\n \"D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt\",\n \"while depth == 0: for delta_x in range(-nei_range, nei_range + 1): for delta_y\",\n \"top 1 grasp prediction within the workspace res = None for kp_pr in\",\n \"cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is\",\n \"kp_pr break if res is None: return [0, 0, 0, 0] f_w, f_h\",\n \"int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map,\",\n \"# Bridge to convert ROS Image type to OpenCV Image type cv_bridge =\",\n \"depth) # pass the image into the network ret = detector.run(inp_image[:, :, :])\",\n \"input from Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth image\",\n \"None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm =\",\n \"return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros\",\n \"# get the current transformation from the camera to aruco tag M_CL, corners\",\n \"channel with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image\",\n \"2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0],\",\n \"import cv2.aruco as aruco import numpy as np import sys import rospy from\",\n \"-0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset =\",\n \"aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale:\",\n \"range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range + 1): nei =\",\n \"into the network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori =\",\n \"ROS Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False\",\n \"tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag from input\",\n \"np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep =\",\n \"import _init_paths import os import json import cv2 import cv2.aruco as aruco import\",\n \"res is None: return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512.\",\n \"= np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450],\",\n \"/ 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2]\",\n \"the robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0.,\",\n \"transformation from the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False)\",\n \"of the detected pixel is 0, check the depth of its neighbors #\",\n \"int(corner[1])] if corner[0] + corner[1] > max: max = corner[0] + corner[1] br_pxl\",\n \"M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize:\",\n \"[pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth !=\",\n \"# default transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369,\",\n \"np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] =\",\n \"240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283,\",\n \"ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may\",\n \"and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts =\",\n \"for category_id, preds in net_output.items(): if len(preds) == 0: continue for pred in\",\n \"detected pixel is 0, check the depth of its neighbors # by counter-clock\",\n \"res = None for kp_pr in kps_pr: f_w, f_h = 640. / 512.,\",\n \"kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no\",\n \"_objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4,\",\n \"45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out def project(pixel,\",\n \"from __future__ import print_function import _init_paths import os import json import cv2 import\",\n \"return [0, 0, 0, 0] # sort by the confidence score kps_pr =\",\n \"rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL\",\n \"depth_data): \\\"\\\"\\\" Save raw RGB and depth input from Kinect V1 :param rgb_data:\",\n \"< -np.pi / 2: orientation = -np.pi - orientation else: orientation = -orientation\",\n \"0: continue for pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0],\",\n \"M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3,\",\n \"pixel: x, y :param M_CL: trans from camera to aruco tag :param cameraMatrix:\"\n]","isConversational":false}}},{"rowIdx":3490,"cells":{"ngram":{"kind":"list like","value":["# kernel = np.array( # [[1, 1, 1], # [1, 5, 1], #","= 1.0): angle=level (h, w) = image.shape[:2] if center is None: center =","level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im =","= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , 0],","= (w / 2, h / 2) # Perform the rotation M =","try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level","= int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width :","in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) #","return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height)","else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'),","augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under')","hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x =","'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'),","= image.copy() # For each pixel in the image, select the pixel #","color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3)","np.array( # [[1, 1, 1], # [1, 5, 1], # [1, 1, 1]],","w) = image.shape[:2] if center is None: center = (w / 2, h","224 , (224,224) or [224,224] padding (bool): padding img before resize. Prevent from","augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y","else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT =","np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size","for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')*",": augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness':","= im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x *","pixel in the image, select the pixel # if the value is less","from the pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img,","'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY'","augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'),","if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :,","# uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) %","in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg","# if the value is less than the threshold. # Otherwise, subtract 255","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel","(width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0],","= random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg -","# no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy()","k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor","pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits","None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize')","augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX'","else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size)","[level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,","img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class","* r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv =","sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] +","0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR","augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate","img before resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray):","= augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv'","level image = image.copy() # For each pixel in the image, select the","0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode","def rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w) =","shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0, 0 ,","width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return","[0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img","if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' :","= cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in","if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' :","rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_","image.shape[:2] if center is None: center = (w / 2, h / 2)","# HSV color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1,","as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images","else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if","adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def","1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,):","= level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ =","height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if","# try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) *","RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout'","cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def","height,width,_ = img.shape delta = height - width if delta > 0: img","return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV","solarize(image, level=128): threshold = level image = image.copy() # For each pixel in","else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if","def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random()","return img def rotate(image, level=45, center = None, scale = 1.0): angle=level (h,","img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode]","the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <= threshold,","= img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return","5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) /","'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else","= cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height","im = im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and","= min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except:","histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im,","else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if","augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k","else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else","# cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level","cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25):","new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width","else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize')","mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 ,","None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else","cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr'",",_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w","def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ =","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ =","= (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION","def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT","or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain]",":, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :,","augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if","augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'),","if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') *","before resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images","% 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x *","0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img =","level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg","- min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img","gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x","image) def posterize(img, level=3): bits = level shift = 8 - bits #","True. Returns: images (np.ndarray): images in target size \"\"\" if padding: height,width,_ =","img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5):","return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def","M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 ,","center = None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center","yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:,","cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 ,","augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'),","img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _","level = min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) #","augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize","else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio =","augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness')","random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg:","than the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <=","self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor):","channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image","width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img =","except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params","else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if","= cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype)","augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v)","image to RGB def solarize(image, level=128): threshold = level image = image.copy() #","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ =","8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] =","if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]],","augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if","'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' :","sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :","= img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height","= cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return","dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x","return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize","np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate =","= RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) #","RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness'","rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level","augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for","else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast')","img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return","kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def","option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M","cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:]","None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else","RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize':","if center is None: center = (w / 2, h / 2) #","cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width,","cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:]","'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else","vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or sgain or","= np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() <","(width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'","translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): #","M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 ,","dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1],","M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array(","[hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im,","if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x","= ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)","= self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img =","- bits # img = img >> shift img = np.left_shift(img,shift) img =","def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img,",":, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y","level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image 'im' with","translate_pixel = int(width * level) M = np.array([[1, 0 , 0], [level, 1","return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width)","= np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment:","else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if","'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else","= np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img","1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ =","[224,224] padding (bool): padding img before resize. Prevent from image distortion. Defaults to","img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value')","image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr","1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),","im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if","height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0])","= img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)","= cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width","augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try:","* level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0,","if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast","= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img):","def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT","'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate'","1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_","level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org","lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat),","augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y","\"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224)","1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs):","augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left","# cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__': augmentation_test()","img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level","yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0,","'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else","'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left'","height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode","cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:]","int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2","sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1, 5, 1],","'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'),","# print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img","0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])","scale = 1.0): angle=level (h, w) = image.shape[:2] if center is None: center","height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( # [[1,",": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width *","np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =","random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg =","else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold =","img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height >","yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug =","size \"\"\" if padding: height,width,_ = img.shape delta = height - width if","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ =","dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im =","else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if","cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr","0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,","im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0])","cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0,","np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv","if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :,","self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None,","return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop }","'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX'","} self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize'","= int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width :","0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel","M = np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 ,","/ 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape","augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v","im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255","int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2","augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' :","option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel","degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def","img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img","else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio =","'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None,","def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None,","augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) *","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_","= img.shape delta = height - width if delta > 0: img =","self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' :","} self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def","sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or sgain","else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert')",": augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under':","sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ =","uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype)","= im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range","img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter =","* (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level):","hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain,","img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h =","cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value =","cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:,","return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0],","img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def","img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:","cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im","RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr",", translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,","= img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height","img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 =","<= threshold, image, 255 - image) def posterize(img, level=3): bits = level shift","RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' :","self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level)",": img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2","threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image,","= cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:,","each pixel in the image, select the pixel # if the value is",": RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None,","0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def","* level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test():","augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else","RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop","the pixel # if the value is less than the threshold. # Otherwise,","={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img =","np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters","= height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255)","image distortion. Defaults to True. Returns: images (np.ndarray): images in target size \"\"\"","HSV color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1,",": RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None,","cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center =","return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB","bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0]",": RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above':","level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img","translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :","translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }","= np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val,","augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224)","def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224","if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' :","if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' :","translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1","in target size \"\"\" if padding: height,width,_ = img.shape delta = height -","= kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img","'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode","'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for","None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX')","return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = {","images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool):","None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize')","level=3): bits = level shift = 8 - bits # img = img","[level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,","M = np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float')",": augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast':",": augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' :","img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8')","cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__':","img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 =","import numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img","np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None, scale","1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13.","'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else","return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits =",": augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' :","if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'),","(w / 2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center,","img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:","= np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains","= img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 =","needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram","'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else","img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_","augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness","delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values","def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if","preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if","else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right')","def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor)","# convert YUV image to RGB def solarize(image, level=128): threshold = level image",", translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)","def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ =","cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width =","level shift = 8 - bits # img = img >> shift img","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1,","h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height =","def posterize(img, level=3): bits = level shift = 8 - bits # img","None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = {","np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img","= img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height","= cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in","13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size","None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left')","if the value is less than the threshold. # Otherwise, subtract 255 from","yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad =","sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0,","'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img,","Defaults to True. Returns: images (np.ndarray): images in target size \"\"\" if padding:","= level shift = 8 - bits # img = img >> shift","is less than the threshold. # Otherwise, subtract 255 from the pixel. return","= np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45,","level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain","'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M","Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv =","images in target size \"\"\" if padding: height,width,_ = img.shape delta = height","x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat",":, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else","0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img =","augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under')","target size. eg: 224 , (224,224) or [224,224] padding (bool): padding img before","bits = level shift = 8 - bits # img = img >>","for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) #","img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def","option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_","_ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug)","pixel # if the value is less than the threshold. # Otherwise, subtract","= augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if","= 8 - bits # img = img >> shift img = np.left_shift(img,shift)","posterize(img, level=3): bits = level shift = 8 - bits # img =","img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height >","hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or","= yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad","augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'),","(w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_","'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else","* r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val =","= np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None,","level=45, center = None, scale = 1.0): angle=level (h, w) = image.shape[:2] if","= cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_","img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_","if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right","np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level:","option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel], [level,","def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0, 0","= img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)","'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None,","translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,","cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))","else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX')","= cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,","'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else","* [hgain, sgain, vgain] + 1 # random gains hue, sat, val =","im = im.copy() # HSV color-space augmentation if hgain or sgain or vgain:","+ 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype =","= level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return","None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate')","np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue,","translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return","= level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ =","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode","1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3,","RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY'",", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'):","else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if","'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in","translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1, 1], #",": RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None,","new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width","augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015,","is None: center = (w / 2, h / 2) # Perform the","img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img","padding (bool): padding img before resize. Prevent from image distortion. Defaults to True.","# 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) #","> width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224))","augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize","if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert","def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M =","=255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params):","'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None,","r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR,","M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={","'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None,","threshold = level image = image.copy() # For each pixel in the image,","rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w) = image.shape[:2]","translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }","random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg)","lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def","1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect'","= np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5):","# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv","angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio","height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if","img.shape delta = height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]],",": augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'),","img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img)","level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape","augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'),","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel =","center is None: center = (w / 2, h / 2) # Perform","scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio =","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0","cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1,","factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None):","RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX'","augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if","cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:,","= c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) #",": cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M =","RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness","img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random()","vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 #","img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) #","> 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255)","img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224]","img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height >","cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue =","np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits = level","Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w,","0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode","bgr=True): im = im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3)","(img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION =","0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return","height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2","r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue,","return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height)",": augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' :","= int(width * level) M = np.array([[1, 0 , 0], [level, 1 ,",", 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT","k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter]","img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 =","target size \"\"\" if padding: height,width,_ = img.shape delta = height - width","lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0,","histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to","img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1])","to RGB def solarize(image, level=128): threshold = level image = image.copy() # For","new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width","= int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width :","cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:]","size. eg: 224 , (224,224) or [224,224] padding (bool): padding img before resize.","= img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img =","sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))","/ 2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle,","in the image, select the pixel # if the value is less than","return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter","or [224,224] padding (bool): padding img before resize. Prevent from image distortion. Defaults","if padding: height,width,_ = img.shape delta = height - width if delta >","else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else","select the pixel # if the value is less than the threshold. #","= int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1 ,","= np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype)","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape","cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :,","height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if","(np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding","augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org)","'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' :","= img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height","center = (w / 2, h / 2) # Perform the rotation M","img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org =","if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers","return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)","(1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if","on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV","else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape","return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on","ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_","in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor +","augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'),","translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ =","Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image, 255 -","Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images in target","level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape","(width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( #","= None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center is","= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25):","={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel =","img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:","= img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)","'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr'","(np.ndarray): images in target size \"\"\" if padding: height,width,_ = img.shape delta =","if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' :","255 - image) def posterize(img, level=3): bits = level shift = 8 -","img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 =","option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , 0], [level,","value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return","cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ =","ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_","=255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return","def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1)","if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain,","= RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img,","# 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' :","None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None,","with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)","np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x","from image distortion. Defaults to True. Returns: images (np.ndarray): images in target size","np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))","# Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image, 255",": RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None,","= img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def","np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio","'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness'","None: center = (w / 2, h / 2) # Perform the rotation","1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for","def solarize(image, level=128): threshold = level image = image.copy() # For each pixel","threshold, image, 255 - image) def posterize(img, level=3): bits = level shift =","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level","(224,224) or [224,224] padding (bool): padding img before resize. Prevent from image distortion.","else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters =","cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level)","= { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if","RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),","new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width","# print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params =","RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0,","* r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv,","255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) #","np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img =","img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): #","= img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h","RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast':","list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img =","img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8')","= np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_","# img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy,","- image) def posterize(img, level=3): bits = level shift = 8 - bits","cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv","img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return",": RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None,","'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert'","v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor)","((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers)","augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'):","yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return","= cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width","augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout","'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, #","# def sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1,","img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_","if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' :","np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_","'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy)","return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor)","class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' :","None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize'","vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype","+ (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter)","if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize","else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if","level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0","1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_","im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR","255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M","= list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level","augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' :","180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2],","lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True,","else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram","# For each pixel in the image, select the pixel # if the","= cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def","RGB def solarize(image, level=128): threshold = level image = image.copy() # For each","= preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit()","cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters:",", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def","return translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1, 1],","np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio","'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under':","= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel],","None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center is None:","clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0])",": RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None,","1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }","= im.copy() # HSV color-space augmentation if hgain or sgain or vgain: r","M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={","img def rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w)","int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2","np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio","0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =",": RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX'","'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy","cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1","0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)","level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape",": augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' :","= 255 return img def rotate(image, level=45, center = None, scale = 1.0):","augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY'","= int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width :","img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8')","degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level,",": augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'),","less than the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image","# random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype #","RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' :","(bool): padding img before resize. Prevent from image distortion. Defaults to True. Returns:","min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: #","level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape","[0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={","if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if","shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0], [0, 0","[1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1])","hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image 'im'","np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img","img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool): padding",": RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None,","return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 ,","augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if","None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None,","0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :","np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center","def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ =","<reponame>dovietchinh/multi-task-classification<filename>source/utils/augmentations.py<gh_stars>0 import numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args:","/ 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated =","'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'),","(h, w) = image.shape[:2] if center is None: center = (w / 2,","None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else","RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize'","= level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ =","def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image","image.copy() # For each pixel in the image, select the pixel # if","(int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool): padding img","# equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) #","tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0]","def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0], [0,","or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1","'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height),","YUV image to RGB def solarize(image, level=128): threshold = level image = image.copy()","the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE)","mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def","0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c =","'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert'","# [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. #","img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None, scale =","= np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size","None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else","RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' :","0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,",">> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor","numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray):","return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape","images (np.ndarray): images in target size \"\"\" if padding: height,width,_ = img.shape delta","= np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float')","1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat,","0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)","img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate =","h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated","if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if","isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers =","cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level)","augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x","bits # img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift)","level=128): threshold = level image = image.copy() # For each pixel in the","if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop }","= int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] =","cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image,","ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_","img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height >","# except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml","augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' :","RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right':","augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above')","0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if","[0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M =","= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'):","{ 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize':","augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level =","to True. Returns: images (np.ndarray): images in target size \"\"\" if padding: height,width,_","borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1,",", 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape",", 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,","cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height =","M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape","255 from the pixel. return np.where(image <= threshold, image, 255 - image) def","'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right':","RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above","random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8","the value is less than the threshold. # Otherwise, subtract 255 from the","256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x *","augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if","'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else","lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0,","if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128):","self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import","and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe:","None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY')","return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width)","= np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float')",", (224,224) or [224,224] padding (bool): padding img before resize. Prevent from image","sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0],","} mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0","height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if","cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width =","contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1,","subtract 255 from the pixel. return np.where(image <= threshold, image, 255 - image)","shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor =","'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape",": RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None,","# [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1,","else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if","# cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size =","int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0],","range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad)","else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio =","int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255","8 - bits # img = img >> shift img = np.left_shift(img,shift) img","0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return","distortion. Defaults to True. Returns: images (np.ndarray): images in target size \"\"\" if","= random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg","'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width","ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_","augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'),","= level image = image.copy() # For each pixel in the image, select",": cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)","def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ =","[level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={","level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0","if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if","'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'),","augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv':","+ img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img","cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold = level","# [[1, 1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32,","factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return","image = image.copy() # For each pixel in the image, select the pixel","= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25):","1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3,","kernel = np.array( # [[1, 1, 1], # [1, 5, 1], # [1,","the pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3):","padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:]","height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 =","np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate","for augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level","# Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M,","height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level,","c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else:","def sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1, 5,","= np.array( # [[1, 1, 1], # [1, 5, 1], # [1, 1,","if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7,","BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if","range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c","# shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img =",":, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv,","height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :","import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug","__init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr')","0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img #","bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold","M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated","print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img =","if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' :","255 return img def rotate(image, level=45, center = None, scale = 1.0): angle=level","Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or","padding img before resize. Prevent from image distortion. Defaults to True. Returns: images",": augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy =","resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images in","augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'),","= image.shape[:2] if center is None: center = (w / 2, h /","augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above':","= img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode =","img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int):","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_","Returns: images (np.ndarray): images in target size \"\"\" if padding: height,width,_ = img.shape","None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness')","= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'):","def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation",": RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None,","equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape","c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize","'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'),","RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under","np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size =","'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout'","r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random","shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy()","lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return","mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size)","For each pixel in the image, select the pixel # if the value","M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 ,","1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def","if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2","mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self,","angle=level (h, w) = image.shape[:2] if center is None: center = (w /","cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im,",": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M,","height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :","} mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img","int(width * level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel],","255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat,","'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None,","img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter","int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2","'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else","= np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float')",": augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' :","[[1, 1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, #","RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org)","cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000):","def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value","height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else:","RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize'","= cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0],","min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img","np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def","image, select the pixel # if the value is less than the threshold.","None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY')","import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple):","None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else","dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img","= self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg')","def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img","return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'):","img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)","= level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ =","'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') #","cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target","= cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if","augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items()","import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size.","= np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate","1.0): angle=level (h, w) = image.shape[:2] if center is None: center = (w","preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 ,","cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image,","[1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2.",", 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' :",": RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None,","the image, select the pixel # if the value is less than the","mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255)","im.copy() # HSV color-space augmentation if hgain or sgain or vgain: r =","value is less than the threshold. # Otherwise, subtract 255 from the pixel.","img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:","None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None,","= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25):","3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val","RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY'","RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate'","= np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat =","< level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im","shift = 8 - bits # img = img >> shift img =","image, 255 - image) def posterize(img, level=3): bits = level shift = 8","random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4):","3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width","augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT","delta = height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values","if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' :","cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue","return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level","augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg +","cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed","= { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'),","img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space","print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params')","None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout')","= img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)",": augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop","self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv","if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' :","- width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img","__call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() #","else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] =","val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256,","\"\"\" if padding: height,width,_ = img.shape delta = height - width if delta","1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype","eg: 224 , (224,224) or [224,224] padding (bool): padding img before resize. Prevent","(max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return","im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no","augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if","rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return","cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True):","option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img","padding: height,width,_ = img.shape delta = height - width if delta > 0:","Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV","invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def","level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape","* level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0,","return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy()",": RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness':","0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue),","r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x","no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() #","cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return","np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() #","self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float')","img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level =","(width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'","{ 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv')","borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,","= img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w =","RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left'","img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im,","convert YUV image to RGB def solarize(image, level=128): threshold = level image =","borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,","2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image,","img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers']",", 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)","2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale)","min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def","borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1"],"string":"[\n \"# kernel = np.array( # [[1, 1, 1], # [1, 5, 1], #\",\n \"= 1.0): angle=level (h, w) = image.shape[:2] if center is None: center =\",\n \"level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im =\",\n \"= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , 0],\",\n \"= (w / 2, h / 2) # Perform the rotation M =\",\n \"try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level\",\n \"= int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width :\",\n \"in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) #\",\n \"return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height)\",\n \"else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'),\",\n \"augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under')\",\n \"hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x =\",\n \"'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'),\",\n \"= image.copy() # For each pixel in the image, select the pixel #\",\n \"color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3)\",\n \"np.array( # [[1, 1, 1], # [1, 5, 1], # [1, 1, 1]],\",\n \"w) = image.shape[:2] if center is None: center = (w / 2, h\",\n \"224 , (224,224) or [224,224] padding (bool): padding img before resize. Prevent from\",\n \"augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y\",\n \"else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT =\",\n \"np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size\",\n \"for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')*\",\n \": augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness':\",\n \"= im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x *\",\n \"pixel in the image, select the pixel # if the value is less\",\n \"from the pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img,\",\n \"'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY'\",\n \"augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'),\",\n \"if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :,\",\n \"# uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) %\",\n \"in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg\",\n \"# if the value is less than the threshold. # Otherwise, subtract 255\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel\",\n \"(width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0],\",\n \"= random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg -\",\n \"# no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy()\",\n \"k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor\",\n \"pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits\",\n \"None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize')\",\n \"augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX'\",\n \"else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size)\",\n \"[level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,\",\n \"img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class\",\n \"* r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv =\",\n \"sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] +\",\n \"0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR\",\n \"augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate\",\n \"img before resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray):\",\n \"= augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv'\",\n \"level image = image.copy() # For each pixel in the image, select the\",\n \"0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode\",\n \"def rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w) =\",\n \"shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0, 0 ,\",\n \"width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return\",\n \"[0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img\",\n \"if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' :\",\n \"= cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in\",\n \"if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' :\",\n \"rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_\",\n \"image.shape[:2] if center is None: center = (w / 2, h / 2)\",\n \"# HSV color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1,\",\n \"as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images\",\n \"else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if\",\n \"adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def\",\n \"1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,):\",\n \"= level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ =\",\n \"height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if\",\n \"# try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) *\",\n \"RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout'\",\n \"cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def\",\n \"height,width,_ = img.shape delta = height - width if delta > 0: img\",\n \"return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV\",\n \"solarize(image, level=128): threshold = level image = image.copy() # For each pixel in\",\n \"else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if\",\n \"def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random()\",\n \"return img def rotate(image, level=45, center = None, scale = 1.0): angle=level (h,\",\n \"img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode]\",\n \"the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <= threshold,\",\n \"= img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return\",\n \"5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) /\",\n \"'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height\",\n \"im = im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and\",\n \"= min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except:\",\n \"histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im,\",\n \"else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if\",\n \"augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k\",\n \"else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else\",\n \"# cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level\",\n \"cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25):\",\n \"new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width\",\n \"else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize')\",\n \"mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 ,\",\n \"None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else\",\n \"cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr'\",\n \",_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w\",\n \"def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ =\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ =\",\n \"= (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION\",\n \"def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT\",\n \"or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain]\",\n \":, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :,\",\n \"augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if\",\n \"augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'),\",\n \"if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') *\",\n \"before resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images\",\n \"% 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x *\",\n \"0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img =\",\n \"level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg\",\n \"- min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img\",\n \"gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x\",\n \"image) def posterize(img, level=3): bits = level shift = 8 - bits #\",\n \"True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_ =\",\n \"img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5):\",\n \"return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def\",\n \"M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 ,\",\n \"center = None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center\",\n \"yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:,\",\n \"cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 ,\",\n \"augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'),\",\n \"img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _\",\n \"level = min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) #\",\n \"augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize\",\n \"else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio =\",\n \"augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness')\",\n \"random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg:\",\n \"than the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <=\",\n \"self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor):\",\n \"channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image\",\n \"width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img =\",\n \"except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params\",\n \"else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if\",\n \"= cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype)\",\n \"augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v)\",\n \"image to RGB def solarize(image, level=128): threshold = level image = image.copy() #\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ =\",\n \"8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] =\",\n \"if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]],\",\n \"augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if\",\n \"'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' :\",\n \"sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :\",\n \"= img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height\",\n \"= cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return\",\n \"dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x\",\n \"return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize\",\n \"np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate =\",\n \"= RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) #\",\n \"RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness'\",\n \"rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level\",\n \"augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for\",\n \"else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast')\",\n \"img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return\",\n \"kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def\",\n \"option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M\",\n \"cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:]\",\n \"None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else\",\n \"RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize':\",\n \"if center is None: center = (w / 2, h / 2) #\",\n \"cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width,\",\n \"cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:]\",\n \"'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else\",\n \"vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or sgain or\",\n \"= np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() <\",\n \"(width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): #\",\n \"M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 ,\",\n \"dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1],\",\n \"M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array(\",\n \"[hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im,\",\n \"if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x\",\n \"= ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\",\n \"= self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img =\",\n \"- bits # img = img >> shift img = np.left_shift(img,shift) img =\",\n \"def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img,\",\n \":, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y\",\n \"level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image 'im' with\",\n \"translate_pixel = int(width * level) M = np.array([[1, 0 , 0], [level, 1\",\n \"return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width)\",\n \"= np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment:\",\n \"else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if\",\n \"'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else\",\n \"= np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img\",\n \"1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ =\",\n \"[224,224] padding (bool): padding img before resize. Prevent from image distortion. Defaults to\",\n \"img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value')\",\n \"image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr\",\n \"1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),\",\n \"im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if\",\n \"height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0])\",\n \"= img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width\",\n \"augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try:\",\n \"* level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0,\",\n \"if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast\",\n \"= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img):\",\n \"def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT\",\n \"'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate'\",\n \"1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_\",\n \"level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org\",\n \"lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat),\",\n \"augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y\",\n \"\\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224)\",\n \"1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs):\",\n \"augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left\",\n \"# cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__': augmentation_test()\",\n \"img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level\",\n \"yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0,\",\n \"'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else\",\n \"'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left'\",\n \"height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode\",\n \"cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:]\",\n \"int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2\",\n \"sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1, 5, 1],\",\n \"'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'),\",\n \"# print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img\",\n \"0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])\",\n \"scale = 1.0): angle=level (h, w) = image.shape[:2] if center is None: center\",\n \"height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( # [[1,\",\n \": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width *\",\n \"np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =\",\n \"random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg =\",\n \"else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold =\",\n \"img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height >\",\n \"yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug =\",\n \"size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height - width if\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ =\",\n \"dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im =\",\n \"else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if\",\n \"cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr\",\n \"0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,\",\n \"im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0])\",\n \"cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0,\",\n \"np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv\",\n \"if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :,\",\n \"self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None,\",\n \"return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop }\",\n \"'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX'\",\n \"} self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize'\",\n \"= int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width :\",\n \"0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel\",\n \"M = np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 ,\",\n \"/ 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape\",\n \"augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v\",\n \"im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255\",\n \"int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2\",\n \"augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' :\",\n \"option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel\",\n \"degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def\",\n \"img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img\",\n \"else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio =\",\n \"'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None,\",\n \"def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None,\",\n \"augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) *\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_\",\n \"= img.shape delta = height - width if delta > 0: img =\",\n \"self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' :\",\n \"} self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def\",\n \"sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or sgain\",\n \"else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert')\",\n \": augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under':\",\n \"sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ =\",\n \"uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype)\",\n \"= im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range\",\n \"img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter =\",\n \"* (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level):\",\n \"hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain,\",\n \"img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h =\",\n \"cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value =\",\n \"cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:,\",\n \"return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0],\",\n \"img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def\",\n \"img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:\",\n \"cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im\",\n \"RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr\",\n \", translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,\",\n \"= img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height\",\n \"img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 =\",\n \"<= threshold, image, 255 - image) def posterize(img, level=3): bits = level shift\",\n \"RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' :\",\n \"self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level)\",\n \": img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2\",\n \"threshold. # Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image,\",\n \"= cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:,\",\n \"each pixel in the image, select the pixel # if the value is\",\n \": RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None,\",\n \"0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def\",\n \"* level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test():\",\n \"augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else\",\n \"RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop\",\n \"the pixel # if the value is less than the threshold. # Otherwise,\",\n \"={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img =\",\n \"np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters\",\n \"= height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255)\",\n \"image distortion. Defaults to True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\"\",\n \"HSV color-space augmentation if hgain or sgain or vgain: r = np.random.uniform(-1, 1,\",\n \": RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None,\",\n \"cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center =\",\n \"return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB\",\n \"bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0]\",\n \": RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above':\",\n \"level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img\",\n \"translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :\",\n \"translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }\",\n \"= np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val,\",\n \"augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224)\",\n \"def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224\",\n \"if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' :\",\n \"if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' :\",\n \"translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1\",\n \"in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height -\",\n \"= kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img\",\n \"'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode\",\n \"'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for\",\n \"None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX')\",\n \"return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = {\",\n \"images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool):\",\n \"None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize')\",\n \"level=3): bits = level shift = 8 - bits # img = img\",\n \"[level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,\",\n \"M = np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \": augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast':\",\n \": augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' :\",\n \"img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8')\",\n \"cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__':\",\n \"img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 =\",\n \"import numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img\",\n \"np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None, scale\",\n \"1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13.\",\n \"'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else\",\n \"return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits =\",\n \": augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' :\",\n \"if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'),\",\n \"(w / 2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center,\",\n \"img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:\",\n \"= np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains\",\n \"= img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 =\",\n \"needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram\",\n \"'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else\",\n \"img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_\",\n \"augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness\",\n \"delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values\",\n \"def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if\",\n \"preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if\",\n \"else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right')\",\n \"def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor)\",\n \"# convert YUV image to RGB def solarize(image, level=128): threshold = level image\",\n \", translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)\",\n \"def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ =\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width =\",\n \"level shift = 8 - bits # img = img >> shift img\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1,\",\n \"h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height =\",\n \"def posterize(img, level=3): bits = level shift = 8 - bits # img\",\n \"None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = {\",\n \"np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img\",\n \"= img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height\",\n \"= cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in\",\n \"13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size\",\n \"None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left')\",\n \"if the value is less than the threshold. # Otherwise, subtract 255 from\",\n \"yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad =\",\n \"sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0,\",\n \"'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img,\",\n \"Defaults to True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if padding:\",\n \"= level shift = 8 - bits # img = img >> shift\",\n \"is less than the threshold. # Otherwise, subtract 255 from the pixel. return\",\n \"= np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45,\",\n \"level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain\",\n \"'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M\",\n \"Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv =\",\n \"images in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height\",\n \"x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat\",\n \":, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else\",\n \"0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img =\",\n \"augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under')\",\n \"target size. eg: 224 , (224,224) or [224,224] padding (bool): padding img before\",\n \"bits = level shift = 8 - bits # img = img >>\",\n \"for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) #\",\n \"img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def\",\n \"option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_\",\n \"_ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug)\",\n \"pixel # if the value is less than the threshold. # Otherwise, subtract\",\n \"= augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if\",\n \"= 8 - bits # img = img >> shift img = np.left_shift(img,shift)\",\n \"posterize(img, level=3): bits = level shift = 8 - bits # img =\",\n \"img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height >\",\n \"hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain or\",\n \"= yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad\",\n \"augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'),\",\n \"(w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_\",\n \"'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else\",\n \"* r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val =\",\n \"= np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None,\",\n \"level=45, center = None, scale = 1.0): angle=level (h, w) = image.shape[:2] if\",\n \"= cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_\",\n \"img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_\",\n \"if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right\",\n \"np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level:\",\n \"option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel], [level,\",\n \"def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0, 0\",\n \"= img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)\",\n \"'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None,\",\n \"translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,\",\n \"cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\",\n \"else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX')\",\n \"= cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,\",\n \"'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else\",\n \"* [hgain, sgain, vgain] + 1 # random gains hue, sat, val =\",\n \"im = im.copy() # HSV color-space augmentation if hgain or sgain or vgain:\",\n \"+ 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype =\",\n \"= level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return\",\n \"None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate')\",\n \"np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue,\",\n \"translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return\",\n \"= level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ =\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode\",\n \"1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3,\",\n \"RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY'\",\n \", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'):\",\n \"else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if\",\n \"'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in\",\n \"translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1, 1], #\",\n \": RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None,\",\n \"new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width\",\n \"augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015,\",\n \"is None: center = (w / 2, h / 2) # Perform the\",\n \"img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img\",\n \"padding (bool): padding img before resize. Prevent from image distortion. Defaults to True.\",\n \"# 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) #\",\n \"> width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224))\",\n \"augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize\",\n \"if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert\",\n \"def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M =\",\n \"=255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params):\",\n \"'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None,\",\n \"r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR,\",\n \"M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={\",\n \"'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None,\",\n \"threshold = level image = image.copy() # For each pixel in the image,\",\n \"rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w) = image.shape[:2]\",\n \"translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }\",\n \"random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg)\",\n \"lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def\",\n \"1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"= np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5):\",\n \"# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv\",\n \"angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio\",\n \"height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if\",\n \"img.shape delta = height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]],\",\n \": augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'),\",\n \"img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img)\",\n \"level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape\",\n \"augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'),\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel =\",\n \"center is None: center = (w / 2, h / 2) # Perform\",\n \"scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio =\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0\",\n \"cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1,\",\n \"factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None):\",\n \"RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX'\",\n \"augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if\",\n \"cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:,\",\n \"= c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) #\",\n \": cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level) M =\",\n \"RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness\",\n \"img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random()\",\n \"vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 #\",\n \"img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) #\",\n \"> 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255)\",\n \"img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224]\",\n \"img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height >\",\n \"cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue =\",\n \"np.where(image <= threshold, image, 255 - image) def posterize(img, level=3): bits = level\",\n \"Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w,\",\n \"0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode\",\n \"bgr=True): im = im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3)\",\n \"(img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION =\",\n \"0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return\",\n \"height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2\",\n \"r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue,\",\n \"return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height)\",\n \": augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' :\",\n \"= int(width * level) M = np.array([[1, 0 , 0], [level, 1 ,\",\n \", 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT\",\n \"k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter]\",\n \"img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2 =\",\n \"target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height - width\",\n \"lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0,\",\n \"histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to\",\n \"img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1])\",\n \"to RGB def solarize(image, level=128): threshold = level image = image.copy() # For\",\n \"new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width\",\n \"= int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width :\",\n \"cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:]\",\n \"size. eg: 224 , (224,224) or [224,224] padding (bool): padding img before resize.\",\n \"= img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img =\",\n \"sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))\",\n \"/ 2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle,\",\n \"in the image, select the pixel # if the value is less than\",\n \"return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter\",\n \"or [224,224] padding (bool): padding img before resize. Prevent from image distortion. Defaults\",\n \"if padding: height,width,_ = img.shape delta = height - width if delta >\",\n \"else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else\",\n \"select the pixel # if the value is less than the threshold. #\",\n \"= int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1 ,\",\n \"= np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :,\",\n \"height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if\",\n \"(np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding\",\n \"augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org)\",\n \"'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' :\",\n \"= img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height\",\n \"center = (w / 2, h / 2) # Perform the rotation M\",\n \"img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org =\",\n \"if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers\",\n \"return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)\",\n \"(1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if\",\n \"on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV\",\n \"else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape\",\n \"return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on\",\n \"ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_\",\n \"in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor +\",\n \"augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'),\",\n \"translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ =\",\n \"Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image, 255 -\",\n \"Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images in target\",\n \"level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape\",\n \"(width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( #\",\n \"= None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center is\",\n \"= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25):\",\n \"={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel =\",\n \"img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:\",\n \"= img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)\",\n \"'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr'\",\n \"(np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta =\",\n \"if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' :\",\n \"255 - image) def posterize(img, level=3): bits = level shift = 8 -\",\n \"img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2 =\",\n \"option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , 0], [level,\",\n \"value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return\",\n \"cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ =\",\n \"ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_\",\n \"=255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return\",\n \"def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1)\",\n \"if hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain,\",\n \"= RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img,\",\n \"# 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' :\",\n \"None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None,\",\n \"with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)\",\n \"np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x\",\n \"from image distortion. Defaults to True. Returns: images (np.ndarray): images in target size\",\n \"np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))\",\n \"# Otherwise, subtract 255 from the pixel. return np.where(image <= threshold, image, 255\",\n \": RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None,\",\n \"= img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def\",\n \"np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio\",\n \"'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness'\",\n \"None: center = (w / 2, h / 2) # Perform the rotation\",\n \"1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for\",\n \"def solarize(image, level=128): threshold = level image = image.copy() # For each pixel\",\n \"threshold, image, 255 - image) def posterize(img, level=3): bits = level shift =\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level\",\n \"(224,224) or [224,224] padding (bool): padding img before resize. Prevent from image distortion.\",\n \"else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters =\",\n \"cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level)\",\n \"= { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if\",\n \"RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\",\n \"new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width\",\n \"# print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params =\",\n \"RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0], [0,\",\n \"* r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv,\",\n \"255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) #\",\n \"np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img =\",\n \"img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): #\",\n \"= img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h\",\n \"RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast':\",\n \"list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img =\",\n \"img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8')\",\n \"= np.array([[1, level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_\",\n \"# img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy,\",\n \"- image) def posterize(img, level=3): bits = level shift = 8 - bits\",\n \"cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv\",\n \"img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return\",\n \": RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None,\",\n \"'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert'\",\n \"v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor)\",\n \"((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers)\",\n \"augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'):\",\n \"yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width\",\n \"augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout\",\n \"'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, #\",\n \"# def sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1,\",\n \"img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_\",\n \"if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' :\",\n \"np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_\",\n \"'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy)\",\n \"return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor)\",\n \"class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' :\",\n \"None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize'\",\n \"vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype\",\n \"+ (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter)\",\n \"if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize\",\n \"else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if\",\n \"level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0\",\n \"1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_\",\n \"im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR\",\n \"255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M\",\n \"= list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level\",\n \"augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' :\",\n \"180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2],\",\n \"lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True,\",\n \"else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram\",\n \"# For each pixel in the image, select the pixel # if the\",\n \"= cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def\",\n \"RGB def solarize(image, level=128): threshold = level image = image.copy() # For each\",\n \"= preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit()\",\n \"cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters:\",\n \", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def\",\n \"return translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1, 1],\",\n \"np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio\",\n \"'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under':\",\n \"= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0 , translate_pixel],\",\n \"None, scale = 1.0): angle=level (h, w) = image.shape[:2] if center is None:\",\n \"clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0])\",\n \": RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None,\",\n \"1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }\",\n \"= im.copy() # HSV color-space augmentation if hgain or sgain or vgain: r\",\n \"M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={\",\n \"img def rotate(image, level=45, center = None, scale = 1.0): angle=level (h, w)\",\n \"int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2\",\n \"np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio\",\n \"0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =\",\n \": RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX'\",\n \"'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy\",\n \"cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1\",\n \"0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)\",\n \"level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape\",\n \": augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' :\",\n \"= 255 return img def rotate(image, level=45, center = None, scale = 1.0):\",\n \"augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY'\",\n \"= int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width :\",\n \"img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8')\",\n \"degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level,\",\n \": augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'),\",\n \"less than the threshold. # Otherwise, subtract 255 from the pixel. return np.where(image\",\n \"# random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype #\",\n \"RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' :\",\n \"(bool): padding img before resize. Prevent from image distortion. Defaults to True. Returns:\",\n \"min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: #\",\n \"level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape\",\n \"[0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={\",\n \"if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if\",\n \"shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0], [0, 0\",\n \"[1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1])\",\n \"hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image 'im'\",\n \"np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img\",\n \"img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool): padding\",\n \": RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None,\",\n \"return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 ,\",\n \"augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if\",\n \"None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None,\",\n \"0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :\",\n \"np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center\",\n \"def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ =\",\n \"<reponame>dovietchinh/multi-task-classification<filename>source/utils/augmentations.py<gh_stars>0 import numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args:\",\n \"/ 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated =\",\n \"'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'),\",\n \"(h, w) = image.shape[:2] if center is None: center = (w / 2,\",\n \"None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else\",\n \"RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize'\",\n \"= level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ =\",\n \"def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR image\",\n \"image.copy() # For each pixel in the image, select the pixel # if\",\n \"(int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding (bool): padding img\",\n \"# equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) #\",\n \"tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0]\",\n \"def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0], [0,\",\n \"or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1\",\n \"'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height),\",\n \"YUV image to RGB def solarize(image, level=128): threshold = level image = image.copy()\",\n \"the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE)\",\n \"mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def\",\n \"0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c =\",\n \"'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert'\",\n \"# [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. #\",\n \"img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center = None, scale =\",\n \"= np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size\",\n \"None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else\",\n \"RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' :\",\n \"0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \">> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor\",\n \"numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray):\",\n \"return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"images (np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta\",\n \"= np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float')\",\n \"1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat,\",\n \"0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)\",\n \"img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate =\",\n \"h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated\",\n \"if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if\",\n \"isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers =\",\n \"cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level)\",\n \"augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x\",\n \"bits # img = img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift)\",\n \"level=128): threshold = level image = image.copy() # For each pixel in the\",\n \"if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop }\",\n \"= int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] =\",\n \"cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image,\",\n \"ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_\",\n \"img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height >\",\n \"# except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml\",\n \"augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' :\",\n \"RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right':\",\n \"augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above')\",\n \"0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if\",\n \"[0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M =\",\n \"= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'):\",\n \"{ 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize':\",\n \"augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level =\",\n \"to True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_\",\n \"borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array( # [[1, 1,\",\n \", 0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape\",\n \", 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height =\",\n \"M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"255 from the pixel. return np.where(image <= threshold, image, 255 - image) def\",\n \"'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right':\",\n \"RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above\",\n \"random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8\",\n \"the value is less than the threshold. # Otherwise, subtract 255 from the\",\n \"256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x *\",\n \"augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if\",\n \"'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else\",\n \"lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0,\",\n \"if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128):\",\n \"self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import\",\n \"and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe:\",\n \"None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY')\",\n \"return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width)\",\n \"= np.array([[1, 0 , 0], [level, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \", (224,224) or [224,224] padding (bool): padding img before resize. Prevent from image\",\n \"sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 , 0],\",\n \"} mode = option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0\",\n \"height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width =\",\n \"contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1,\",\n \"subtract 255 from the pixel. return np.where(image <= threshold, image, 255 - image)\",\n \"shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor =\",\n \"'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \": RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None,\",\n \"# [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1,\",\n \"else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if\",\n \"# cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size =\",\n \"int(width * level) M = np.array([[1, 0 , translate_pixel], [level, 1 , 0],\",\n \"range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad)\",\n \"else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio =\",\n \"int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255\",\n \"8 - bits # img = img >> shift img = np.left_shift(img,shift) img\",\n \"0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return\",\n \"distortion. Defaults to True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if\",\n \"= random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() # try: min_arg,max_arg\",\n \"'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width\",\n \"ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_\",\n \"augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'),\",\n \"= level image = image.copy() # For each pixel in the image, select\",\n \": cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)\",\n \"def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_ =\",\n \"[level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={\",\n \"level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0\",\n \"if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if\",\n \"'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'),\",\n \"augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv':\",\n \"+ img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img\",\n \"cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold = level\",\n \"# [[1, 1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32,\",\n \"factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return\",\n \"image = image.copy() # For each pixel in the image, select the pixel\",\n \"= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25):\",\n \"1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3,\",\n \"kernel = np.array( # [[1, 1, 1], # [1, 5, 1], # [1,\",\n \"the pixel. return np.where(image <= threshold, image, 255 - image) def posterize(img, level=3):\",\n \"padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:]\",\n \"height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 =\",\n \"np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate\",\n \"for augmenter in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level\",\n \"# Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M,\",\n \"height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level,\",\n \"c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else:\",\n \"def sharpness(img,): # kernel = np.array( # [[1, 1, 1], # [1, 5,\",\n \"= np.array( # [[1, 1, 1], # [1, 5, 1], # [1, 1,\",\n \"if random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7,\",\n \"BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if\",\n \"range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c\",\n \"# shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img =\",\n \":, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv,\",\n \"height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \"import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug\",\n \"__init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr')\",\n \"0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img #\",\n \"bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128): threshold\",\n \"M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated\",\n \"print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img =\",\n \"if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' :\",\n \"255 return img def rotate(image, level=45, center = None, scale = 1.0): angle=level\",\n \"Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224) or\",\n \"padding img before resize. Prevent from image distortion. Defaults to True. Returns: images\",\n \": augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy =\",\n \"resize. Prevent from image distortion. Defaults to True. Returns: images (np.ndarray): images in\",\n \"augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'),\",\n \"= image.shape[:2] if center is None: center = (w / 2, h /\",\n \"augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above':\",\n \"= img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode =\",\n \"img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int):\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_\",\n \"Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape\",\n \"None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness')\",\n \"= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'):\",\n \"def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation\",\n \": RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None,\",\n \"equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape\",\n \"c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize\",\n \"'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'),\",\n \"RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under\",\n \"np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size =\",\n \"'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout'\",\n \"r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random\",\n \"shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy()\",\n \"lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return\",\n \"mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size)\",\n \"For each pixel in the image, select the pixel # if the value\",\n \"M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0 ,\",\n \"1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def\",\n \"if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2\",\n \"mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self,\",\n \"angle=level (h, w) = image.shape[:2] if center is None: center = (w /\",\n \"cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im,\",\n \": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M,\",\n \"height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \"} mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img\",\n \"int(width * level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel],\",\n \"255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat,\",\n \"'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None,\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter\",\n \"int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2\",\n \"'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else\",\n \"= np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \": augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' :\",\n \"[[1, 1, 1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, #\",\n \"RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org)\",\n \"cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000):\",\n \"def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value\",\n \"height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else:\",\n \"RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize'\",\n \"= cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0],\",\n \"min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level img\",\n \"np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def\",\n \"image, select the pixel # if the value is less than the threshold.\",\n \"None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY')\",\n \"import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple):\",\n \"None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else\",\n \"dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img\",\n \"= self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg')\",\n \"def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img\",\n \"return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'):\",\n \"img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)\",\n \"= level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ =\",\n \"'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') #\",\n \"cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target\",\n \"= cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if\",\n \"augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items()\",\n \"import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size.\",\n \"= np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate\",\n \"1.0): angle=level (h, w) = image.shape[:2] if center is None: center = (w\",\n \"preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 ,\",\n \"cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image,\",\n \"[1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2.\",\n \", 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \": RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None,\",\n \"the image, select the pixel # if the value is less than the\",\n \"mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255)\",\n \"im.copy() # HSV color-space augmentation if hgain or sgain or vgain: r =\",\n \"value is less than the threshold. # Otherwise, subtract 255 from the pixel.\",\n \"img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:\",\n \"None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None,\",\n \"= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25):\",\n \"3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val\",\n \"RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY'\",\n \"RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate'\",\n \"= np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat =\",\n \"< level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im\",\n \"shift = 8 - bits # img = img >> shift img =\",\n \"image, 255 - image) def posterize(img, level=3): bits = level shift = 8\",\n \"random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4):\",\n \"3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width\",\n \"augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT\",\n \"delta = height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values\",\n \"if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' :\",\n \"cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue\",\n \"return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level\",\n \"augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg +\",\n \"cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed\",\n \"= { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'),\",\n \"img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space\",\n \"print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params')\",\n \"None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout')\",\n \"= img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)\",\n \": augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop\",\n \"self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv\",\n \"if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' :\",\n \"- width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img\",\n \"__call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() #\",\n \"else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] =\",\n \"val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256,\",\n \"\\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height - width if delta\",\n \"1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype\",\n \"eg: 224 , (224,224) or [224,224] padding (bool): padding img before resize. Prevent\",\n \"(max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return\",\n \"im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no\",\n \"augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if\",\n \"rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return\",\n \"cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True):\",\n \"option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img\",\n \"padding: height,width,_ = img.shape delta = height - width if delta > 0:\",\n \"Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV\",\n \"invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def\",\n \"level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape\",\n \"* level) M = np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0,\",\n \"return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy()\",\n \": RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness':\",\n \"0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue),\",\n \"r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x\",\n \"no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() #\",\n \"cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return\",\n \"np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() #\",\n \"self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float')\",\n \"img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level =\",\n \"(width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"{ 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv')\",\n \"borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \"= img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w =\",\n \"RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left'\",\n \"img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def augment_hsv(im,\",\n \"convert YUV image to RGB def solarize(image, level=128): threshold = level image =\",\n \"borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \"2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image,\",\n \"img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers']\",\n \", 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)\",\n \"2, h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale)\",\n \"min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def\",\n \"borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1\"\n]","isConversational":false}}},{"rowIdx":3491,"cells":{"ngram":{"kind":"list like","value":["REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules","__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None ## generate","Intel Corporation. All rights reserved.<BR> # # This program and the accompanying materials","THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS,","class CapsuleAfile (CapsuleData): ## The constructor # # @param self The object pointer","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if","IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR","= None self.FvName = None ## generate FFS capsule data # # @param","def __init__(self) : self.Ffs = None self.FvName = None ## generate FFS capsule","http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN","None ## generate FD capsule data # # @param self The object pointer","Afile class for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor","be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE","the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:","# class CapsuleFv (CapsuleData): ## The constructor # # @param self The object","and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)","# generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR>","= None ## generate Afile capsule data # # @param self The object","IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT","return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma","ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # #","KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs","int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,","# # This program and the accompanying materials # are licensed and made","int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],","= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:","pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],","FD capsule data # # @param self The object pointer # @retval string","WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #","# which accompanies this distribution. The full text of the license may be","# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON","= None ## generate AnyFile capsule data # # @param self The object","FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName","data # # class CapsuleFv (CapsuleData): ## The constructor # # @param self","name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # # class CapsuleFd","None self.FdName = None self.CapsuleName = None ## generate FD capsule data #","capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param","ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,","= None ## generate FFS capsule data # # @param self The object","the full authenticated info size. From first bytes of MonotonicCount to last bytes","generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> #","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # # class","Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # # class","data # # class CapsuleAfile (CapsuleData): ## The constructor # # @param self","= None self.FdName = None self.CapsuleName = None ## generate FD capsule data","CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1) typedef struct","object pointer # def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName","of the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM","# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],","pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##","Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],","class CapsuleFd (CapsuleData): ## The constructor # # @param self The object pointer","AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor","# are licensed and made available under the terms and conditions of the","= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')","# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule","name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj","= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return","self.Ffs = None self.FvName = None ## generate FFS capsule data # #","CapsuleFv (CapsuleData): ## The constructor # # @param self The object pointer #","16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),","not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not","data # # class CapsuleData: ## The constructor # # @param self The","= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =","SaveFileOnChange import uuid ## base class for capsule data # # class CapsuleData:","return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule","GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)","to last bytes of certificate. # the 32 bit is the MonotonicCount, dwLength,","0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH',","found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD","# @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for","16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,","name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header","None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None","= None ## generate FV capsule data # # @param self The object","IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable","Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and","pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32","None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []","The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile","= None ## generate FD capsule data # # @param self The object","## The constructor # # @param self The object pointer # def __init__(self)","self The object pointer # def __init__(self) : self.Ffs = None self.FvName =","bytes of MonotonicCount to last bytes of certificate. # the 32 bit is","Append file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=","None self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]):","self.FileName ## Afile class for capsule data # # class CapsuleAfile (CapsuleData): ##","return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule","UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):","def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None ##","terms and conditions of the BSD License # which accompanies this distribution. The",": self.Ffs = None self.FdName = None self.CapsuleName = None ## generate FD","(c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and the","UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None","# # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This","class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor #","def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper())","+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()","@param self The object pointer # def __init__(self) : self.Ffs = None self.FvName","= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD","GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##","int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if","= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # # class","object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =","name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #","at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE","self.FileName = None ## generate AnyFile capsule data # # @param self The","FFS capsule data # # @param self The object pointer # @retval string","= None self.CapsuleName = None ## generate FD capsule data # # @param","defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;","ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import","of MonotonicCount to last bytes of certificate. # the 32 bit is the","GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # # class CapsuleAfile","object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')","string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload","from Common.Misc import SaveFileOnChange import uuid ## base class for capsule data #","FV capsule data # # @param self The object pointer # @retval string","From first bytes of MonotonicCount to last bytes of certificate. # the 32","FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for","-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile","FvFile ## FD class for capsule data # # class CapsuleFd (CapsuleData): ##","None ## generate Afile capsule data # # @param self The object pointer","@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile","The constructor # # @param self The object pointer # def __init__(self) :","return self.FileName ## Afile class for capsule data # # class CapsuleAfile (CapsuleData):","pointer # def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName =","pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class","of the BSD License # which accompanies this distribution. The full text of","# # class CapsuleFv (CapsuleData): ## The constructor # # @param self The","= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],","Common.Misc import SaveFileOnChange import uuid ## base class for capsule data # #","= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =","FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)","pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==","pass ## FFS class for capsule data # # class CapsuleFfs (CapsuleData): ##","self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include","self The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self):","capsule data # # class CapsuleData: ## The constructor # # @param self","16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),","The full text of the license may be found at # http://opensource.org/licenses/bsd-license.php #","licensed and made available under the terms and conditions of the BSD License","if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #","self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data","CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object pointer #","self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if","if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')","== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return","PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, #","int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer","__init__(self) : self.Ffs = None self.FileName = None ## generate Afile capsule data","self.Ffs = None self.FvName = None self.CapsuleName = None ## generate FV capsule","in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None","VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =","[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False","__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None ## generate","the terms and conditions of the BSD License # which accompanies this distribution.","AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #","class for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor #","BSD License # which accompanies this distribution. The full text of the license","-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =","UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;","self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance","VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #","None self.CapsuleName = None ## generate FV capsule data # # @param self","else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #","FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##","wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize =","LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY","= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =","= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer =","# def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None","constructor # # @param self The object pointer # def __init__(self) : self.Ffs","self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile","self.FvName = None ## generate FFS capsule data # # @param self The","class CapsuleData: ## The constructor # # @param self The object pointer def","for capsule data # # class CapsuleFd (CapsuleData): ## The constructor # #","content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()","## generate FV capsule data # # @param self The object pointer #","16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:","Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile","os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated info size.","pass ## generate capsule data # # @param self The object pointer def","# @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile","= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class","def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None ##","## Afile class for capsule data # # class CapsuleAfile (CapsuleData): ## The","FFS class for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor","AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'","int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],","+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file","EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None","License # which accompanies this distribution. The full text of the license may","= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =","the header is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID","file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the","FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return","for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor # #","## base class for capsule data # # class CapsuleData: ## The constructor","All rights reserved.<BR> # # This program and the accompanying materials # are","generate FD capsule data # # @param self The object pointer # @retval","from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os from","UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2","file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class","[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer = None","to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if","## FFS class for capsule data # # class CapsuleFfs (CapsuleData): ## The","The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if","generate AnyFile capsule data # # @param self The object pointer # @retval","# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is","## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO","self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =","16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),","class for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor #","FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # #","self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile","def __init__(self) : self.Ffs = None self.FileName = None ## generate Afile capsule","16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),","uuid ## base class for capsule data # # class CapsuleData: ## The","capsule data # # class CapsuleAfile (CapsuleData): ## The constructor # # @param","UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64","GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #","FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile","data # # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self","this distribution. The full text of the license may be found at #","object pointer # def __init__(self) : self.Ffs = None self.FileName = None ##","'''Generate payload file, the header is defined below: #pragma pack(1) typedef struct {","= None self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,","# ## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import","int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)","//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =","if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if","license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED","Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),","# # class CapsuleFfs (CapsuleData): ## The constructor # # @param self The","# def __init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile","= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile","if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName","Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in","''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex","GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)","GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc","= None self.FvName = None self.CapsuleName = None ## generate FV capsule data","The object pointer def __init__(self): pass ## generate capsule data # # @param","GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None","pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()","struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32","the BSD License # which accompanies this distribution. The full text of the","def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())","self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version","file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys():","@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate","# # Append file content to the structure # ImageFile = open(self.ImageFile, 'rb')","0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =","MonotonicCount to last bytes of certificate. # the 32 bit is the MonotonicCount,","False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'","# # @param self The object pointer # def __init__(self) : self.Ffs =","int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,","self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:","os from Common.Misc import SaveFileOnChange import uuid ## base class for capsule data","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile","UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version","data # # class CapsuleFfs (CapsuleData): ## The constructor # # @param self","import GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc import","The constructor # # @param self The object pointer def __init__(self): pass ##","None self.FileName = None ## generate Afile capsule data # # @param self","# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # #","self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')","#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3];","## The constructor # # @param self The object pointer def __init__(self): pass","return FfsFile ## FV class for capsule data # # class CapsuleFv (CapsuleData):","{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;","# # @param self The object pointer def __init__(self): pass ## generate capsule","# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],","are licensed and made available under the terms and conditions of the BSD","FfsFile ## FV class for capsule data # # class CapsuleFv (CapsuleData): ##","UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =","# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program","info size. From first bytes of MonotonicCount to last bytes of certificate. #","None self.FileName = None ## generate AnyFile capsule data # # @param self","object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')","ImageFileSize need include the full authenticated info size. From first bytes of MonotonicCount","Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os","return FvFile ## FD class for capsule data # # class CapsuleFd (CapsuleData):","# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =","'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #","object pointer # def __init__(self) : self.Ffs = None self.FvName = None ##","FV class for capsule data # # class CapsuleFv (CapsuleData): ## The constructor","= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),","@param self The object pointer def __init__(self): pass ## generate capsule data #","AuthData: # the ImageFileSize need include the full authenticated info size. From first","the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0","distribution. The full text of the license may be found at # http://opensource.org/licenses/bsd-license.php","= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =","FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data","data # # class CapsuleFd (CapsuleData): ## The constructor # # @param self","import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import","pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if","UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName","} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId =","full text of the license may be found at # http://opensource.org/licenses/bsd-license.php # #","def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex =","__init__(self): pass ## generate capsule data # # @param self The object pointer","0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],","@param self The object pointer # def __init__(self) : self.Ffs = None self.FdName","self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile","certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType","16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=","class for capsule data # # class CapsuleFv (CapsuleData): ## The constructor #","32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure","Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct","= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =","## generate FFS capsule data # # @param self The object pointer #","FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # #","below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8","# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =","None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:","The object pointer # def __init__(self) : self.Ffs = None self.FileName = None","__init__(self) : self.Ffs = None self.FvName = None ## generate FFS capsule data","16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )","and conditions of the BSD License # which accompanies this distribution. The full","def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined","StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile","GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:","None ## generate FFS capsule data # # @param self The object pointer","Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append","name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for","# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return","struct import pack import os from Common.Misc import SaveFileOnChange import uuid ## base","pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class","# def __init__(self) : self.Ffs = None self.FvName = None ## generate FFS","32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32","if AuthData: # the ImageFileSize need include the full authenticated info size. From","VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])","ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,","not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =","None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class",": self.Ffs = None self.FvName = None self.CapsuleName = None ## generate FV","= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =","import pack import os from Common.Misc import SaveFileOnChange import uuid ## base class","(CapsuleData): ## The constructor # # @param self The object pointer # def","the accompanying materials # are licensed and made available under the terms and","None self.FvName = None ## generate FFS capsule data # # @param self","16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),","under the terms and conditions of the BSD License # which accompanies this","class for capsule data # # class CapsuleFd (CapsuleData): ## The constructor #","# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =","may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER","@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class","GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc import SaveFileOnChange","= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not","CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #","OR IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable import","is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8","file, the header is defined below: #pragma pack(1) typedef struct { UINT32 Version;","self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need","# the ImageFileSize need include the full authenticated info size. From first bytes","pointer def __init__(self): pass ## generate capsule data # # @param self The","int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,","Corporation. All rights reserved.<BR> # # This program and the accompanying materials #","capsule data # # class CapsuleFv (CapsuleData): ## The constructor # # @param","self.CapsuleName = None ## generate FV capsule data # # @param self The","self.FileName = None ## generate Afile capsule data # # @param self The","GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer","# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\"","full authenticated info size. From first bytes of MonotonicCount to last bytes of","FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # #","generate Afile capsule data # # @param self The object pointer # @retval","string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##","BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.","import uuid ## base class for capsule data # # class CapsuleData: ##","class CapsuleFv (CapsuleData): ## The constructor # # @param self The object pointer","accompanying materials # are licensed and made available under the terms and conditions","self.Ffs = None self.FileName = None ## generate AnyFile capsule data # #","self The object pointer def __init__(self): pass ## generate capsule data # #","reserved.<BR> # # This program and the accompanying materials # are licensed and","if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:","data # # @param self The object pointer # @retval string Generated file","## generate AnyFile capsule data # # @param self The object pointer #","structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),","include the full authenticated info size. From first bytes of MonotonicCount to last","self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()","class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object pointer","pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content","for capsule data # # class CapsuleData: ## The constructor # # @param","16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),","and the accompanying materials # are licensed and made available under the terms","# This program and the accompanying materials # are licensed and made available","def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data","self The object pointer # def __init__(self) : self.Ffs = None self.FileName =","of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and","= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated info","UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def","None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None","## FV class for capsule data # # class CapsuleFv (CapsuleData): ## The","header is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;",") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS","BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF","made available under the terms and conditions of the BSD License # which","2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and the accompanying","return FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):","class CapsuleFfs (CapsuleData): ## The constructor # # @param self The object pointer","available under the terms and conditions of the BSD License # which accompanies","for capsule data # # class CapsuleFv (CapsuleData): ## The constructor # #","self The object pointer # def __init__(self) : self.Ffs = None self.FdName =","payload file, the header is defined below: #pragma pack(1) typedef struct { UINT32","int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer","need include the full authenticated info size. From first bytes of MonotonicCount to","StringIO from struct import pack import os from Common.Misc import SaveFileOnChange import uuid","CapsuleData: ## The constructor # # @param self The object pointer def __init__(self):","# @param self The object pointer def __init__(self): pass ## generate capsule data","text of the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE","The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return","= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =","capsule data # # @param self The object pointer def GenCapsuleSubItem(self): pass ##","Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import","generate capsule data # # @param self The object pointer def GenCapsuleSubItem(self): pass","FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile","the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=","'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=","FD class for capsule data # # class CapsuleFd (CapsuleData): ## The constructor","UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in","conditions of the BSD License # which accompanies this distribution. The full text","import os from Common.Misc import SaveFileOnChange import uuid ## base class for capsule","## generate capsule data # # @param self The object pointer def GenCapsuleSubItem(self):","string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()","# class CapsuleAfile (CapsuleData): ## The constructor # # @param self The object","THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS","None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []","EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced","def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex","int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,","# # @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class","Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,","AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to","## generate FD capsule data # # @param self The object pointer #","# class CapsuleFfs (CapsuleData): ## The constructor # # @param self The object","def __init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile capsule","ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],","constructor # # @param self The object pointer def __init__(self): pass ## generate","GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class CapsuleFfs (CapsuleData):","ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed","GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile","@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule","## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The","None ## generate FV capsule data # # @param self The object pointer","# # class CapsuleAfile (CapsuleData): ## The constructor # # @param self The","else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #","accompanies this distribution. The full text of the license may be found at","FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for","+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile =","typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;","self.Ffs = None self.FileName = None ## generate Afile capsule data # #","FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()","ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed =","def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # # class","file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():","OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules #","capsule data # # class CapsuleFd (CapsuleData): ## The constructor # # @param","The object pointer # def __init__(self) : self.Ffs = None self.FvName = None","# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object","UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs from","CapsuleFd (CapsuleData): ## The constructor # # @param self The object pointer #","self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # # class CapsuleFv","self.CapsuleName = None ## generate FD capsule data # # @param self The","string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()","which accompanies this distribution. The full text of the license may be found","object pointer # def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName","self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)","@file # generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights","self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid","__init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile capsule data","size. From first bytes of MonotonicCount to last bytes of certificate. # the","## @file # generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All","file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data","# @param self The object pointer # @retval string Generated file name #","generate FV capsule data # # @param self The object pointer # @retval","last bytes of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,","wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize","in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =","= None self.FileName = None ## generate AnyFile capsule data # # @param","+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill","EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable","CapsuleAfile (CapsuleData): ## The constructor # # @param self The object pointer #","int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],","AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure","capsule data # # @param self The object pointer # @retval string Generated","FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:","# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from","None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None","program and the accompanying materials # are licensed and made available under the","# class CapsuleData: ## The constructor # # @param self The object pointer","self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance","self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile","# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #","ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated","# # class CapsuleFd (CapsuleData): ## The constructor # # @param self The","import StringIO from struct import pack import os from Common.Misc import SaveFileOnChange import","if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =","int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],","# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS","self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return","self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer","capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # #","pointer # def __init__(self) : self.Ffs = None self.FileName = None ## generate","Afile capsule data # # @param self The object pointer # @retval string","pack import os from Common.Misc import SaveFileOnChange import uuid ## base class for","if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True","self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if","OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import","object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName","the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS","= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid","= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return","'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'","## FD class for capsule data # # class CapsuleFd (CapsuleData): ## The","class for capsule data # # class CapsuleData: ## The constructor # #","Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;","# def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None","bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize","Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule","## generate Afile capsule data # # @param self The object pointer #","# @param self The object pointer # def __init__(self) : self.Ffs = None","DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES","object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data # #","dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:","self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1)","for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor # #","# @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:","# class CapsuleFd (CapsuleData): ## The constructor # # @param self The object","@param self The object pointer # def __init__(self) : self.Ffs = None self.FileName","FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data","Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],","= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile","'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full","if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize","uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile = open(self.ImageFile,","MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if","VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer","self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer = None def","0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],","None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:","AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER","from struct import pack import os from Common.Misc import SaveFileOnChange import uuid ##","def GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class CapsuleFfs","This program and the accompanying materials # are licensed and made available under","The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data #","CapsuleFfs (CapsuleData): ## The constructor # # @param self The object pointer #","@param self The object pointer # @retval string Generated file name # def","v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId","file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()","AnyFile capsule data # # @param self The object pointer # @retval string","WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##","__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex = None","first bytes of MonotonicCount to last bytes of certificate. # the 32 bit","structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile","AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #","if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the","materials # are licensed and made available under the terms and conditions of","os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),","# # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The","not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize","16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,","GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex =",": self.Ffs = None self.FvName = None ## generate FFS capsule data #","if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =","and made available under the terms and conditions of the BSD License #","# @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):","# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize","in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =","class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1) typedef","AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the","self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount","int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],","None self.FvName = None self.CapsuleName = None ## generate FV capsule data #","None self.CapsuleName = None ## generate FD capsule data # # @param self","bytes of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType","authenticated info size. From first bytes of MonotonicCount to last bytes of certificate.","# @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:","= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not","# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),","def __init__(self): pass ## generate capsule data # # @param self The object","the ImageFileSize need include the full authenticated info size. From first bytes of","== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName","# Append file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer","self.Ffs = None self.FdName = None self.CapsuleName = None ## generate FD capsule","self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed","= None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =","FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ##","int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16),","reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''","pointer # def __init__(self) : self.Ffs = None self.FvName = None ## generate","UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }","None ## generate AnyFile capsule data # # @param self The object pointer","Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()","object pointer def __init__(self): pass ## generate capsule data # # @param self","= '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the","string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for","# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack","for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor # #","pointer # def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =","self.FvName = None self.CapsuleName = None ## generate FV capsule data # #","= None self.CapsuleName = None ## generate FV capsule data # # @param",": self.Ffs = None self.FileName = None ## generate AnyFile capsule data #","self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if","is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =","self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer","open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer","data # # @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS","16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=","base class for capsule data # # class CapsuleData: ## The constructor #","16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,","# # class CapsuleData: ## The constructor # # @param self The object","= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',","self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16),","import SaveFileOnChange import uuid ## base class for capsule data # # class","= None self.FileName = None ## generate Afile capsule data # # @param","capsule data # # class CapsuleFfs (CapsuleData): ## The constructor # # @param","generate FFS capsule data # # @param self The object pointer # @retval","The object pointer # def __init__(self) : self.Ffs = None self.FdName = None","Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV","# # @param self The object pointer # @retval string Generated file name",": self.Ffs = None self.FileName = None ## generate Afile capsule data #","self.FdName = None self.CapsuleName = None ## generate FD capsule data # #","# def __init__(self) : self.Ffs = None self.FileName = None ## generate Afile","rights reserved.<BR> # # This program and the accompanying materials # are licensed"],"string":"[\n \"REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules\",\n \"__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None ## generate\",\n \"Intel Corporation. All rights reserved.<BR> # # This program and the accompanying materials\",\n \"THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS,\",\n \"class CapsuleAfile (CapsuleData): ## The constructor # # @param self The object pointer\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR\",\n \"= None self.FvName = None ## generate FFS capsule data # # @param\",\n \"def __init__(self) : self.Ffs = None self.FvName = None ## generate FFS capsule\",\n \"http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN\",\n \"None ## generate FD capsule data # # @param self The object pointer\",\n \"Afile class for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor\",\n \"be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE\",\n \"the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:\",\n \"# class CapsuleFv (CapsuleData): ## The constructor # # @param self The object\",\n \"and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)\",\n \"# generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR>\",\n \"= None ## generate Afile capsule data # # @param self The object\",\n \"IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma\",\n \"ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # #\",\n \"KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs\",\n \"int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,\",\n \"# # This program and the accompanying materials # are licensed and made\",\n \"int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],\",\n \"= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:\",\n \"pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],\",\n \"FD capsule data # # @param self The object pointer # @retval string\",\n \"WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #\",\n \"# which accompanies this distribution. The full text of the license may be\",\n \"# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON\",\n \"= None ## generate AnyFile capsule data # # @param self The object\",\n \"FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName\",\n \"data # # class CapsuleFv (CapsuleData): ## The constructor # # @param self\",\n \"name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # # class CapsuleFd\",\n \"None self.FdName = None self.CapsuleName = None ## generate FD capsule data #\",\n \"capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param\",\n \"ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,\",\n \"= None ## generate FFS capsule data # # @param self The object\",\n \"the full authenticated info size. From first bytes of MonotonicCount to last bytes\",\n \"generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> #\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # # class\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # # class\",\n \"data # # class CapsuleAfile (CapsuleData): ## The constructor # # @param self\",\n \"= None self.FdName = None self.CapsuleName = None ## generate FD capsule data\",\n \"CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1) typedef struct\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName\",\n \"of the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM\",\n \"# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##\",\n \"Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],\",\n \"class CapsuleFd (CapsuleData): ## The constructor # # @param self The object pointer\",\n \"AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor\",\n \"# are licensed and made available under the terms and conditions of the\",\n \"= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')\",\n \"# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule\",\n \"name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj\",\n \"= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return\",\n \"self.Ffs = None self.FvName = None ## generate FFS capsule data # #\",\n \"CapsuleFv (CapsuleData): ## The constructor # # @param self The object pointer #\",\n \"16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),\",\n \"not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not\",\n \"data # # class CapsuleData: ## The constructor # # @param self The\",\n \"= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =\",\n \"SaveFileOnChange import uuid ## base class for capsule data # # class CapsuleData:\",\n \"return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)\",\n \"to last bytes of certificate. # the 32 bit is the MonotonicCount, dwLength,\",\n \"0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH',\",\n \"found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD\",\n \"# @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for\",\n \"16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,\",\n \"name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header\",\n \"None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None\",\n \"= None ## generate FV capsule data # # @param self The object\",\n \"IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable\",\n \"Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and\",\n \"pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32\",\n \"None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []\",\n \"The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile\",\n \"= None ## generate FD capsule data # # @param self The object\",\n \"## The constructor # # @param self The object pointer # def __init__(self)\",\n \"self The object pointer # def __init__(self) : self.Ffs = None self.FvName =\",\n \"bytes of MonotonicCount to last bytes of certificate. # the 32 bit is\",\n \"Append file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=\",\n \"None self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]):\",\n \"self.FileName ## Afile class for capsule data # # class CapsuleAfile (CapsuleData): ##\",\n \"return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule\",\n \"UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):\",\n \"def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None ##\",\n \"terms and conditions of the BSD License # which accompanies this distribution. The\",\n \": self.Ffs = None self.FdName = None self.CapsuleName = None ## generate FD\",\n \"(c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and the\",\n \"UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None\",\n \"# # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This\",\n \"class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor #\",\n \"def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper())\",\n \"+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()\",\n \"@param self The object pointer # def __init__(self) : self.Ffs = None self.FvName\",\n \"= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##\",\n \"int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if\",\n \"= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # # class\",\n \"object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =\",\n \"name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #\",\n \"at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE\",\n \"self.FileName = None ## generate AnyFile capsule data # # @param self The\",\n \"FFS capsule data # # @param self The object pointer # @retval string\",\n \"= None self.CapsuleName = None ## generate FD capsule data # # @param\",\n \"defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;\",\n \"ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import\",\n \"of MonotonicCount to last bytes of certificate. # the 32 bit is the\",\n \"GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # # class CapsuleAfile\",\n \"object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload\",\n \"from Common.Misc import SaveFileOnChange import uuid ## base class for capsule data #\",\n \"FV capsule data # # @param self The object pointer # @retval string\",\n \"From first bytes of MonotonicCount to last bytes of certificate. # the 32\",\n \"FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for\",\n \"-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile\",\n \"FvFile ## FD class for capsule data # # class CapsuleFd (CapsuleData): ##\",\n \"None ## generate Afile capsule data # # @param self The object pointer\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile\",\n \"The constructor # # @param self The object pointer # def __init__(self) :\",\n \"return self.FileName ## Afile class for capsule data # # class CapsuleAfile (CapsuleData):\",\n \"pointer # def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName =\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class\",\n \"of the BSD License # which accompanies this distribution. The full text of\",\n \"# # class CapsuleFv (CapsuleData): ## The constructor # # @param self The\",\n \"= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],\",\n \"Common.Misc import SaveFileOnChange import uuid ## base class for capsule data # #\",\n \"= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =\",\n \"FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==\",\n \"pass ## FFS class for capsule data # # class CapsuleFfs (CapsuleData): ##\",\n \"self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include\",\n \"self The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self):\",\n \"capsule data # # class CapsuleData: ## The constructor # # @param self\",\n \"16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),\",\n \"The full text of the license may be found at # http://opensource.org/licenses/bsd-license.php #\",\n \"licensed and made available under the terms and conditions of the BSD License\",\n \"if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #\",\n \"self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data\",\n \"CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object pointer #\",\n \"self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if\",\n \"if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')\",\n \"== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return\",\n \"PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, #\",\n \"int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer\",\n \"__init__(self) : self.Ffs = None self.FileName = None ## generate Afile capsule data\",\n \"self.Ffs = None self.FvName = None self.CapsuleName = None ## generate FV capsule\",\n \"in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None\",\n \"VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =\",\n \"[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False\",\n \"__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None ## generate\",\n \"the terms and conditions of the BSD License # which accompanies this distribution.\",\n \"AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #\",\n \"class for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor #\",\n \"BSD License # which accompanies this distribution. The full text of the license\",\n \"-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =\",\n \"UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;\",\n \"self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance\",\n \"VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #\",\n \"None self.CapsuleName = None ## generate FV capsule data # # @param self\",\n \"else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #\",\n \"FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##\",\n \"wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize =\",\n \"LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY\",\n \"= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =\",\n \"= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer =\",\n \"# def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName = None\",\n \"constructor # # @param self The object pointer # def __init__(self) : self.Ffs\",\n \"self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile\",\n \"self.FvName = None ## generate FFS capsule data # # @param self The\",\n \"class CapsuleData: ## The constructor # # @param self The object pointer def\",\n \"for capsule data # # class CapsuleFd (CapsuleData): ## The constructor # #\",\n \"content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()\",\n \"## generate FV capsule data # # @param self The object pointer #\",\n \"16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:\",\n \"Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile\",\n \"os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated info size.\",\n \"pass ## generate capsule data # # @param self The object pointer def\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile\",\n \"= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class\",\n \"def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None ##\",\n \"## Afile class for capsule data # # class CapsuleAfile (CapsuleData): ## The\",\n \"FFS class for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor\",\n \"AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'\",\n \"int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],\",\n \"+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file\",\n \"EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None\",\n \"License # which accompanies this distribution. The full text of the license may\",\n \"= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =\",\n \"the header is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the\",\n \"FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return\",\n \"for capsule data # # class CapsuleAfile (CapsuleData): ## The constructor # #\",\n \"## base class for capsule data # # class CapsuleData: ## The constructor\",\n \"All rights reserved.<BR> # # This program and the accompanying materials # are\",\n \"generate FD capsule data # # @param self The object pointer # @retval\",\n \"from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os from\",\n \"UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2\",\n \"file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class\",\n \"[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer = None\",\n \"to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if\",\n \"## FFS class for capsule data # # class CapsuleFfs (CapsuleData): ## The\",\n \"The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if\",\n \"generate AnyFile capsule data # # @param self The object pointer # @retval\",\n \"# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is\",\n \"## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO\",\n \"self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =\",\n \"16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),\",\n \"class for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor #\",\n \"FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data # #\",\n \"self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile\",\n \"def __init__(self) : self.Ffs = None self.FileName = None ## generate Afile capsule\",\n \"16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),\",\n \"uuid ## base class for capsule data # # class CapsuleData: ## The\",\n \"capsule data # # class CapsuleAfile (CapsuleData): ## The constructor # # @param\",\n \"UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64\",\n \"GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #\",\n \"FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile\",\n \"data # # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self\",\n \"this distribution. The full text of the license may be found at #\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FileName = None ##\",\n \"'''Generate payload file, the header is defined below: #pragma pack(1) typedef struct {\",\n \"= None self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,\",\n \"# ## # Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import\",\n \"int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)\",\n \"//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =\",\n \"if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if\",\n \"license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED\",\n \"Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),\",\n \"# # class CapsuleFfs (CapsuleData): ## The constructor # # @param self The\",\n \"# def __init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile\",\n \"= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile\",\n \"if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in\",\n \"''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)\",\n \"GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc\",\n \"= None self.FvName = None self.CapsuleName = None ## generate FV capsule data\",\n \"The object pointer def __init__(self): pass ## generate capsule data # # @param\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()\",\n \"struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32\",\n \"the BSD License # which accompanies this distribution. The full text of the\",\n \"def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())\",\n \"self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version\",\n \"file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys():\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate\",\n \"# # Append file content to the structure # ImageFile = open(self.ImageFile, 'rb')\",\n \"0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =\",\n \"MonotonicCount to last bytes of certificate. # the 32 bit is the MonotonicCount,\",\n \"False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'\",\n \"# # @param self The object pointer # def __init__(self) : self.Ffs =\",\n \"int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,\",\n \"self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:\",\n \"os from Common.Misc import SaveFileOnChange import uuid ## base class for capsule data\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile\",\n \"UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version\",\n \"data # # class CapsuleFfs (CapsuleData): ## The constructor # # @param self\",\n \"import GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc import\",\n \"The constructor # # @param self The object pointer def __init__(self): pass ##\",\n \"None self.FileName = None ## generate Afile capsule data # # @param self\",\n \"# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # #\",\n \"self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')\",\n \"#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3];\",\n \"## The constructor # # @param self The object pointer def __init__(self): pass\",\n \"return FfsFile ## FV class for capsule data # # class CapsuleFv (CapsuleData):\",\n \"{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;\",\n \"# # @param self The object pointer def __init__(self): pass ## generate capsule\",\n \"# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],\",\n \"are licensed and made available under the terms and conditions of the BSD\",\n \"FfsFile ## FV class for capsule data # # class CapsuleFv (CapsuleData): ##\",\n \"UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =\",\n \"# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # # This program\",\n \"info size. From first bytes of MonotonicCount to last bytes of certificate. #\",\n \"None self.FileName = None ## generate AnyFile capsule data # # @param self\",\n \"object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')\",\n \"ImageFileSize need include the full authenticated info size. From first bytes of MonotonicCount\",\n \"Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import os\",\n \"return FvFile ## FD class for capsule data # # class CapsuleFd (CapsuleData):\",\n \"# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =\",\n \"'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FvName = None ##\",\n \"FV class for capsule data # # class CapsuleFv (CapsuleData): ## The constructor\",\n \"= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),\",\n \"@param self The object pointer def __init__(self): pass ## generate capsule data #\",\n \"AuthData: # the ImageFileSize need include the full authenticated info size. From first\",\n \"the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0\",\n \"distribution. The full text of the license may be found at # http://opensource.org/licenses/bsd-license.php\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =\",\n \"FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data\",\n \"data # # class CapsuleFd (CapsuleData): ## The constructor # # @param self\",\n \"import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if\",\n \"UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName\",\n \"} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId =\",\n \"full text of the license may be found at # http://opensource.org/licenses/bsd-license.php # #\",\n \"def __init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex =\",\n \"__init__(self): pass ## generate capsule data # # @param self The object pointer\",\n \"0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],\",\n \"@param self The object pointer # def __init__(self) : self.Ffs = None self.FdName\",\n \"self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile\",\n \"certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType\",\n \"16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=\",\n \"class for capsule data # # class CapsuleFv (CapsuleData): ## The constructor #\",\n \"32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure\",\n \"Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct\",\n \"= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =\",\n \"## generate FFS capsule data # # @param self The object pointer #\",\n \"FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # #\",\n \"below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8\",\n \"# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =\",\n \"None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:\",\n \"The object pointer # def __init__(self) : self.Ffs = None self.FileName = None\",\n \"__init__(self) : self.Ffs = None self.FvName = None ## generate FFS capsule data\",\n \"16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )\",\n \"and conditions of the BSD License # which accompanies this distribution. The full\",\n \"def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined\",\n \"StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile\",\n \"GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:\",\n \"None ## generate FFS capsule data # # @param self The object pointer\",\n \"Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append\",\n \"name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return\",\n \"struct import pack import os from Common.Misc import SaveFileOnChange import uuid ## base\",\n \"pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class\",\n \"# def __init__(self) : self.Ffs = None self.FvName = None ## generate FFS\",\n \"32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32\",\n \"if AuthData: # the ImageFileSize need include the full authenticated info size. From\",\n \"VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])\",\n \"ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,\",\n \"not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =\",\n \"None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class\",\n \": self.Ffs = None self.FvName = None self.CapsuleName = None ## generate FV\",\n \"= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =\",\n \"import pack import os from Common.Misc import SaveFileOnChange import uuid ## base class\",\n \"(CapsuleData): ## The constructor # # @param self The object pointer # def\",\n \"the accompanying materials # are licensed and made available under the terms and\",\n \"None self.FvName = None ## generate FFS capsule data # # @param self\",\n \"16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),\",\n \"under the terms and conditions of the BSD License # which accompanies this\",\n \"class for capsule data # # class CapsuleFd (CapsuleData): ## The constructor #\",\n \"# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =\",\n \"may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class\",\n \"GenFdsGlobalVariable import StringIO from struct import pack import os from Common.Misc import SaveFileOnChange\",\n \"= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not\",\n \"CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #\",\n \"OR IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable import\",\n \"is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8\",\n \"file, the header is defined below: #pragma pack(1) typedef struct { UINT32 Version;\",\n \"self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need\",\n \"# the ImageFileSize need include the full authenticated info size. From first bytes\",\n \"pointer def __init__(self): pass ## generate capsule data # # @param self The\",\n \"int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,\",\n \"Corporation. All rights reserved.<BR> # # This program and the accompanying materials #\",\n \"capsule data # # class CapsuleFv (CapsuleData): ## The constructor # # @param\",\n \"self.CapsuleName = None ## generate FV capsule data # # @param self The\",\n \"self.FileName = None ## generate Afile capsule data # # @param self The\",\n \"GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer\",\n \"# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\"\",\n \"full authenticated info size. From first bytes of MonotonicCount to last bytes of\",\n \"FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data # #\",\n \"generate Afile capsule data # # @param self The object pointer # @retval\",\n \"string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##\",\n \"BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.\",\n \"import uuid ## base class for capsule data # # class CapsuleData: ##\",\n \"class CapsuleFv (CapsuleData): ## The constructor # # @param self The object pointer\",\n \"accompanying materials # are licensed and made available under the terms and conditions\",\n \"self.Ffs = None self.FileName = None ## generate AnyFile capsule data # #\",\n \"self The object pointer def __init__(self): pass ## generate capsule data # #\",\n \"reserved.<BR> # # This program and the accompanying materials # are licensed and\",\n \"if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:\",\n \"data # # @param self The object pointer # @retval string Generated file\",\n \"## generate AnyFile capsule data # # @param self The object pointer #\",\n \"structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),\",\n \"include the full authenticated info size. From first bytes of MonotonicCount to last\",\n \"self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()\",\n \"class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object pointer\",\n \"pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content\",\n \"for capsule data # # class CapsuleData: ## The constructor # # @param\",\n \"16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),\",\n \"and the accompanying materials # are licensed and made available under the terms\",\n \"# This program and the accompanying materials # are licensed and made available\",\n \"def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule data\",\n \"self The object pointer # def __init__(self) : self.Ffs = None self.FileName =\",\n \"of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and\",\n \"= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated info\",\n \"UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def\",\n \"None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None\",\n \"## FV class for capsule data # # class CapsuleFv (CapsuleData): ## The\",\n \"header is defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;\",\n \") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS\",\n \"BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF\",\n \"made available under the terms and conditions of the BSD License # which\",\n \"2007-2017, Intel Corporation. All rights reserved.<BR> # # This program and the accompanying\",\n \"return FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):\",\n \"class CapsuleFfs (CapsuleData): ## The constructor # # @param self The object pointer\",\n \"available under the terms and conditions of the BSD License # which accompanies\",\n \"for capsule data # # class CapsuleFv (CapsuleData): ## The constructor # #\",\n \"self The object pointer # def __init__(self) : self.Ffs = None self.FdName =\",\n \"payload file, the header is defined below: #pragma pack(1) typedef struct { UINT32\",\n \"int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer\",\n \"need include the full authenticated info size. From first bytes of MonotonicCount to\",\n \"StringIO from struct import pack import os from Common.Misc import SaveFileOnChange import uuid\",\n \"CapsuleData: ## The constructor # # @param self The object pointer def __init__(self):\",\n \"# @param self The object pointer def __init__(self): pass ## generate capsule data\",\n \"text of the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE\",\n \"The object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return\",\n \"= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =\",\n \"capsule data # # @param self The object pointer def GenCapsuleSubItem(self): pass ##\",\n \"Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import\",\n \"generate capsule data # # @param self The object pointer def GenCapsuleSubItem(self): pass\",\n \"FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile\",\n \"the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=\",\n \"'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=\",\n \"FD class for capsule data # # class CapsuleFd (CapsuleData): ## The constructor\",\n \"UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in\",\n \"conditions of the BSD License # which accompanies this distribution. The full text\",\n \"import os from Common.Misc import SaveFileOnChange import uuid ## base class for capsule\",\n \"## generate capsule data # # @param self The object pointer def GenCapsuleSubItem(self):\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()\",\n \"# class CapsuleAfile (CapsuleData): ## The constructor # # @param self The object\",\n \"THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS\",\n \"None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []\",\n \"EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced\",\n \"def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex\",\n \"int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,\",\n \"# # @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,\",\n \"AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to\",\n \"## generate FD capsule data # # @param self The object pointer #\",\n \"# class CapsuleFfs (CapsuleData): ## The constructor # # @param self The object\",\n \"def __init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile capsule\",\n \"ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],\",\n \"constructor # # @param self The object pointer def __init__(self): pass ## generate\",\n \"GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class CapsuleFfs (CapsuleData):\",\n \"ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed\",\n \"GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile\",\n \"@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule\",\n \"## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ## The\",\n \"None ## generate FV capsule data # # @param self The object pointer\",\n \"# # class CapsuleAfile (CapsuleData): ## The constructor # # @param self The\",\n \"else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #\",\n \"accompanies this distribution. The full text of the license may be found at\",\n \"FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for\",\n \"+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile =\",\n \"typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;\",\n \"self.Ffs = None self.FileName = None ## generate Afile capsule data # #\",\n \"FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()\",\n \"ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed =\",\n \"def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data # # class\",\n \"file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():\",\n \"OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules #\",\n \"capsule data # # class CapsuleFd (CapsuleData): ## The constructor # # @param\",\n \"The object pointer # def __init__(self) : self.Ffs = None self.FvName = None\",\n \"# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The object\",\n \"UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"EITHER EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs from\",\n \"CapsuleFd (CapsuleData): ## The constructor # # @param self The object pointer #\",\n \"self.Ffs.GenFfs() return FfsFile ## FV class for capsule data # # class CapsuleFv\",\n \"self.CapsuleName = None ## generate FD capsule data # # @param self The\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()\",\n \"which accompanies this distribution. The full text of the license may be found\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName\",\n \"self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)\",\n \"@file # generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights\",\n \"self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid\",\n \"__init__(self) : self.Ffs = None self.FileName = None ## generate AnyFile capsule data\",\n \"size. From first bytes of MonotonicCount to last bytes of certificate. # the\",\n \"## @file # generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data\",\n \"# @param self The object pointer # @retval string Generated file name #\",\n \"generate FV capsule data # # @param self The object pointer # @retval\",\n \"last bytes of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,\",\n \"wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =\",\n \"= None self.FileName = None ## generate AnyFile capsule data # # @param\",\n \"+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill\",\n \"EXPRESS OR IMPLIED. # ## # Import Modules # import Ffs from GenFdsGlobalVariable\",\n \"CapsuleAfile (CapsuleData): ## The constructor # # @param self The object pointer #\",\n \"int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],\",\n \"AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure\",\n \"capsule data # # @param self The object pointer # @retval string Generated\",\n \"FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:\",\n \"# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from\",\n \"None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None\",\n \"program and the accompanying materials # are licensed and made available under the\",\n \"# class CapsuleData: ## The constructor # # @param self The object pointer\",\n \"self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance\",\n \"self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile\",\n \"# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #\",\n \"ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full authenticated\",\n \"# # class CapsuleFd (CapsuleData): ## The constructor # # @param self The\",\n \"import StringIO from struct import pack import os from Common.Misc import SaveFileOnChange import\",\n \"if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =\",\n \"int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],\",\n \"# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS\",\n \"self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return\",\n \"self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer\",\n \"capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.<BR> # #\",\n \"pointer # def __init__(self) : self.Ffs = None self.FileName = None ## generate\",\n \"Afile capsule data # # @param self The object pointer # @retval string\",\n \"pack import os from Common.Misc import SaveFileOnChange import uuid ## base class for\",\n \"if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True\",\n \"self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if\",\n \"OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import\",\n \"object pointer # @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName\",\n \"the license may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS\",\n \"= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid\",\n \"= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return\",\n \"'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'\",\n \"## FD class for capsule data # # class CapsuleFd (CapsuleData): ## The\",\n \"class for capsule data # # class CapsuleData: ## The constructor # #\",\n \"Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;\",\n \"# def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None\",\n \"bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule\",\n \"## generate Afile capsule data # # @param self The object pointer #\",\n \"# @param self The object pointer # def __init__(self) : self.Ffs = None\",\n \"DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data # #\",\n \"dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:\",\n \"self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1)\",\n \"for capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor # #\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:\",\n \"# class CapsuleFd (CapsuleData): ## The constructor # # @param self The object\",\n \"@param self The object pointer # def __init__(self) : self.Ffs = None self.FileName\",\n \"FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data\",\n \"Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile\",\n \"'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full\",\n \"if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize\",\n \"uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure # ImageFile = open(self.ImageFile,\",\n \"MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if\",\n \"VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer\",\n \"self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer = None def\",\n \"0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],\",\n \"None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:\",\n \"AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER\",\n \"from struct import pack import os from Common.Misc import SaveFileOnChange import uuid ##\",\n \"def GenCapsuleSubItem(self): pass ## FFS class for capsule data # # class CapsuleFfs\",\n \"This program and the accompanying materials # are licensed and made available under\",\n \"The object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data #\",\n \"CapsuleFfs (CapsuleData): ## The constructor # # @param self The object pointer #\",\n \"@param self The object pointer # @retval string Generated file name # def\",\n \"v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId\",\n \"file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()\",\n \"AnyFile capsule data # # @param self The object pointer # @retval string\",\n \"WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##\",\n \"__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex = None\",\n \"first bytes of MonotonicCount to last bytes of certificate. # the 32 bit\",\n \"structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile\",\n \"AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #\",\n \"if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the\",\n \"materials # are licensed and made available under the terms and conditions of\",\n \"os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),\",\n \"# # class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The\",\n \"not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize\",\n \"16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,\",\n \"GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex =\",\n \": self.Ffs = None self.FvName = None ## generate FFS capsule data #\",\n \"if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =\",\n \"and made available under the terms and conditions of the BSD License #\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):\",\n \"# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =\",\n \"class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma pack(1) typedef\",\n \"AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the\",\n \"self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount\",\n \"int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],\",\n \"None self.FvName = None self.CapsuleName = None ## generate FV capsule data #\",\n \"None self.CapsuleName = None ## generate FD capsule data # # @param self\",\n \"bytes of certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType\",\n \"authenticated info size. From first bytes of MonotonicCount to last bytes of certificate.\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:\",\n \"= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not\",\n \"# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),\",\n \"def __init__(self): pass ## generate capsule data # # @param self The object\",\n \"the ImageFileSize need include the full authenticated info size. From first bytes of\",\n \"== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName\",\n \"# Append file content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer\",\n \"self.Ffs = None self.FdName = None self.CapsuleName = None ## generate FD capsule\",\n \"self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed\",\n \"= None self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =\",\n \"FdFile ## AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData): ##\",\n \"int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16),\",\n \"reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''\",\n \"pointer # def __init__(self) : self.Ffs = None self.FvName = None ## generate\",\n \"UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }\",\n \"None ## generate AnyFile capsule data # # @param self The object pointer\",\n \"Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()\",\n \"object pointer def __init__(self): pass ## generate capsule data # # @param self\",\n \"= '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for\",\n \"# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack\",\n \"for capsule data # # class CapsuleFfs (CapsuleData): ## The constructor # #\",\n \"pointer # def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =\",\n \"self.FvName = None self.CapsuleName = None ## generate FV capsule data # #\",\n \"= None self.CapsuleName = None ## generate FV capsule data # # @param\",\n \": self.Ffs = None self.FileName = None ## generate AnyFile capsule data #\",\n \"self.MonotonicCount = None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if\",\n \"is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =\",\n \"self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer\",\n \"open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer\",\n \"data # # @param self The object pointer def GenCapsuleSubItem(self): pass ## FFS\",\n \"16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=\",\n \"base class for capsule data # # class CapsuleData: ## The constructor #\",\n \"16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,\",\n \"# # class CapsuleData: ## The constructor # # @param self The object\",\n \"= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',\",\n \"self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16),\",\n \"import SaveFileOnChange import uuid ## base class for capsule data # # class\",\n \"= None self.FileName = None ## generate Afile capsule data # # @param\",\n \"capsule data # # class CapsuleFfs (CapsuleData): ## The constructor # # @param\",\n \"generate FFS capsule data # # @param self The object pointer # @retval\",\n \"The object pointer # def __init__(self) : self.Ffs = None self.FdName = None\",\n \"Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV\",\n \"# # @param self The object pointer # @retval string Generated file name\",\n \": self.Ffs = None self.FileName = None ## generate Afile capsule data #\",\n \"self.FdName = None self.CapsuleName = None ## generate FD capsule data # #\",\n \"# def __init__(self) : self.Ffs = None self.FileName = None ## generate Afile\",\n \"rights reserved.<BR> # # This program and the accompanying materials # are licensed\"\n]","isConversational":false}}},{"rowIdx":3492,"cells":{"ngram":{"kind":"list like","value":["y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,","as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #","Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi","open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))","= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)","ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\")","plt import numpy as np # Read data size = [] time =","line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num.","data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')","file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot","in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax","fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()","matplotlib.pyplot as plt import numpy as np # Read data size = []","# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',","time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time","Read data size = [] time = [] with open(\"pi_linear.txt\") as file: for","numpy as np # Read data size = [] time = [] with","import numpy as np # Read data size = [] time = []","for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data","[] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y = line.split(',')","= [] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y =","import matplotlib.pyplot as plt import numpy as np # Read data size =","size = [] time = [] with open(\"pi_linear.txt\") as file: for line in","[] time = [] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x,","x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()","with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))","data size = [] time = [] with open(\"pi_linear.txt\") as file: for line","np # Read data size = [] time = [] with open(\"pi_linear.txt\") as","size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',","file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =","as plt import numpy as np # Read data size = [] time","= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\") plt.show()","time = [] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y","# Read data size = [] time = [] with open(\"pi_linear.txt\") as file:","as np # Read data size = [] time = [] with open(\"pi_linear.txt\")","line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,","= [] time = [] with open(\"pi_linear.txt\") as file: for line in file.readlines():"],"string":"[\n \"y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,\",\n \"as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #\",\n \"Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi\",\n \"open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))\",\n \"= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)\",\n \"ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\")\",\n \"plt import numpy as np # Read data size = [] time =\",\n \"line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num.\",\n \"data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')\",\n \"file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot\",\n \"in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax\",\n \"fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()\",\n \"matplotlib.pyplot as plt import numpy as np # Read data size = []\",\n \"# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',\",\n \"time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time\",\n \"Read data size = [] time = [] with open(\\\"pi_linear.txt\\\") as file: for\",\n \"numpy as np # Read data size = [] time = [] with\",\n \"import numpy as np # Read data size = [] time = []\",\n \"for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data\",\n \"[] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y = line.split(',')\",\n \"= [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y =\",\n \"import matplotlib.pyplot as plt import numpy as np # Read data size =\",\n \"size = [] time = [] with open(\\\"pi_linear.txt\\\") as file: for line in\",\n \"[] time = [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x,\",\n \"x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()\",\n \"with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))\",\n \"data size = [] time = [] with open(\\\"pi_linear.txt\\\") as file: for line\",\n \"np # Read data size = [] time = [] with open(\\\"pi_linear.txt\\\") as\",\n \"size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',\",\n \"file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =\",\n \"as plt import numpy as np # Read data size = [] time\",\n \"= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\") plt.show()\",\n \"time = [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y\",\n \"# Read data size = [] time = [] with open(\\\"pi_linear.txt\\\") as file:\",\n \"as np # Read data size = [] time = [] with open(\\\"pi_linear.txt\\\")\",\n \"line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,\",\n \"= [] time = [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines():\"\n]","isConversational":false}}},{"rowIdx":3493,"cells":{"ngram":{"kind":"list like","value":["the request did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The",":type partition_id: int :param error_code: The result error, or zero if there was","throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for which",":param topic: The topic name :type topic: str :param partition_result: The results for","def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type",":param partition_result: The results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic =","error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class","quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an empty array",":type error_code: ErrorCode :param error_message: The result message, or null if there was","error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message =","partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult]","no error. :type error_code: ErrorCode :param error_message: The result message, or null if","if there was no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code","The results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result","results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result =","milliseconds for which the request was throttled due to a quota violation, or","violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an","Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The","partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self,",":type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms:","ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in","ErrorCode :param error_message: The result message, or null if there was no error.","typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import","the requester did not have permission and the request asks for all partitions.","from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int","null if there was no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id","which the request was throttled due to a quota violation, or zero if","partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type topic: str :param partition_result:","The topic name :type topic: str :param partition_result: The results for each partition",":param replica_election_results: The election results, or an empty array if the requester did","topic: str :param partition_result: The results for each partition :type partition_result: List[PartitionResult] \"\"\"",":type topic: str :param partition_result: The results for each partition :type partition_result: List[PartitionResult]","\"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult]","or null if there was no error. :type error_message: Optional[str] \"\"\" self.partition_id =","the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms","partition_id: The partition id :type partition_id: int :param error_code: The result error, or","zero if the request did not violate any quota. :type throttle_time_ms: int :param","PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode,","ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class","throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results:","duration in milliseconds for which the request was throttled due to a quota",":type throttle_time_ms: int :param replica_election_results: The election results, or an empty array if","str :param partition_result: The results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic","the request was throttled due to a quota violation, or zero if the","Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id:","partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic","Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult:","request was throttled due to a quota violation, or zero if the request","zero if there was no error. :type error_code: ErrorCode :param error_message: The result","error. :type error_code: ErrorCode :param error_message: The result message, or null if there","array if the requester did not have permission and the request asks for","due to a quota violation, or zero if the request did not violate","__init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type topic:","error, or zero if there was no error. :type error_code: ErrorCode :param error_message:","topic: The topic name :type topic: str :param partition_result: The results for each","error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id:","ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message:","int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id:","int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]):","throttle_time_ms: The duration in milliseconds for which the request was throttled due to","request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results","ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The","was no error. :type error_code: ErrorCode :param error_message: The result message, or null","The election results, or an empty array if the requester did not have","self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key:","results, or an empty array if the requester did not have permission and","List[PartitionResult]): \"\"\" :param topic: The topic name :type topic: str :param partition_result: The","partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message:","message, or null if there was no error. :type error_message: Optional[str] \"\"\" self.partition_id","import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id:","if the requester did not have permission and the request asks for all","..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self,","List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results:","throttle_time_ms: int :param replica_election_results: The election results, or an empty array if the","did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results,","error_code: ErrorCode :param error_message: The result message, or null if there was no","id :type partition_id: int :param error_code: The result error, or zero if there","ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int,","import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData","replica_election_results: The election results, or an empty array if the requester did not","api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms:","replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for which the request",":param error_message: The result message, or null if there was no error. :type","The result message, or null if there was no error. :type error_message: Optional[str]","= partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def","for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results = replica_election_results","= error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self,","or zero if there was no error. :type error_code: ErrorCode :param error_message: The","error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id: int :param error_code:","throttled due to a quota violation, or zero if the request did not","ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int,","or an empty array if the requester did not have permission and the","error_message: The result message, or null if there was no error. :type error_message:",":type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message","partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int","def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition","Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id: int :param error_code: The","topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type topic: str","from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def","<gh_stars>0 from typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from",":param error_code: The result error, or zero if there was no error. :type","result error, or zero if there was no error. :type error_code: ErrorCode :param","have permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\"","partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type","if there was no error. :type error_code: ErrorCode :param error_message: The result message,","The partition id :type partition_id: int :param error_code: The result error, or zero","result message, or null if there was no error. :type error_message: Optional[str] \"\"\"","error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\"","election results, or an empty array if the requester did not have permission","partition id :type partition_id: int :param error_code: The result error, or zero if","ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param","for which the request was throttled due to a quota violation, or zero","no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message","request did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election","__init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id","self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS","ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str]","in milliseconds for which the request was throttled due to a quota violation,","The result error, or zero if there was no error. :type error_code: ErrorCode","= ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration","any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an empty","for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result",":param throttle_time_ms: The duration in milliseconds for which the request was throttled due","permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms","...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code:","List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name","name :type topic: str :param partition_result: The results for each partition :type partition_result:","was throttled due to a quota violation, or zero if the request did","str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type topic: str :param","asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results =","class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\"","not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or","quota violation, or zero if the request did not violate any quota. :type","if the request did not violate any quota. :type throttle_time_ms: int :param replica_election_results:","did not have permission and the request asks for all partitions. :type replica_election_results:","The duration in milliseconds for which the request was throttled due to a","= partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result:","List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for which the request was","int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for which the","ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param","and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms =","requester did not have permission and the request asks for all partitions. :type","List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param","a quota violation, or zero if the request did not violate any quota.","error_code: The result error, or zero if there was no error. :type error_code:","= topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey]","topic name :type topic: str :param partition_result: The results for each partition :type","class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms:","__init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for","or zero if the request did not violate any quota. :type throttle_time_ms: int","\"\"\" :param partition_id: The partition id :type partition_id: int :param error_code: The result","int :param error_code: The result error, or zero if there was no error.","import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode","List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult:","\"\"\" :param throttle_time_ms: The duration in milliseconds for which the request was throttled","each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class","self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str","\"\"\" :param topic: The topic name :type topic: str :param partition_result: The results","partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData):",":param partition_id: The partition id :type partition_id: int :param error_code: The result error,","there was no error. :type error_code: ErrorCode :param error_message: The result message, or","error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]):","an empty array if the requester did not have permission and the request","topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] =","was no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code","error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic:","int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]):","ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id: int :param","partition_id: int :param error_code: The result error, or zero if there was no","there was no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code =","def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds","empty array if the requester did not have permission and the request asks","int :param replica_election_results: The election results, or an empty array if the requester","\"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic:","replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\"","topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic:","error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id: int","not have permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult]","class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code:","self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str,","to a quota violation, or zero if the request did not violate any","violation, or zero if the request did not violate any quota. :type throttle_time_ms:","str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The","from typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base","= error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result:","partition_result: The results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic","self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def"],"string":"[\n \"the request did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The\",\n \":type partition_id: int :param error_code: The result error, or zero if there was\",\n \"throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which\",\n \":param topic: The topic name :type topic: str :param partition_result: The results for\",\n \"def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type\",\n \":param partition_result: The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic =\",\n \"error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class\",\n \"quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an empty array\",\n \":type error_code: ErrorCode :param error_message: The result message, or null if there was\",\n \"error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message =\",\n \"partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult]\",\n \"no error. :type error_code: ErrorCode :param error_message: The result message, or null if\",\n \"if there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code\",\n \"The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result\",\n \"results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result =\",\n \"milliseconds for which the request was throttled due to a quota violation, or\",\n \"violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an\",\n \"Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The\",\n \"partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self,\",\n \":type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms:\",\n \"ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in\",\n \"ErrorCode :param error_message: The result message, or null if there was no error.\",\n \"typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import\",\n \"the requester did not have permission and the request asks for all partitions.\",\n \"from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int\",\n \"null if there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id\",\n \"which the request was throttled due to a quota violation, or zero if\",\n \"partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic: str :param partition_result:\",\n \"The topic name :type topic: str :param partition_result: The results for each partition\",\n \":param replica_election_results: The election results, or an empty array if the requester did\",\n \"topic: str :param partition_result: The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\"\",\n \":type topic: str :param partition_result: The results for each partition :type partition_result: List[PartitionResult]\",\n \"\\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult]\",\n \"or null if there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id =\",\n \"the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms\",\n \"partition_id: The partition id :type partition_id: int :param error_code: The result error, or\",\n \"zero if the request did not violate any quota. :type throttle_time_ms: int :param\",\n \"PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode,\",\n \"ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class\",\n \"throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results:\",\n \"duration in milliseconds for which the request was throttled due to a quota\",\n \":type throttle_time_ms: int :param replica_election_results: The election results, or an empty array if\",\n \"str :param partition_result: The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic\",\n \"the request was throttled due to a quota violation, or zero if the\",\n \"Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id:\",\n \"partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic\",\n \"Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult:\",\n \"request was throttled due to a quota violation, or zero if the request\",\n \"zero if there was no error. :type error_code: ErrorCode :param error_message: The result\",\n \"error. :type error_code: ErrorCode :param error_message: The result message, or null if there\",\n \"array if the requester did not have permission and the request asks for\",\n \"due to a quota violation, or zero if the request did not violate\",\n \"__init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic:\",\n \"error, or zero if there was no error. :type error_code: ErrorCode :param error_message:\",\n \"topic: The topic name :type topic: str :param partition_result: The results for each\",\n \"error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id:\",\n \"ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message:\",\n \"int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id:\",\n \"int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]):\",\n \"throttle_time_ms: The duration in milliseconds for which the request was throttled due to\",\n \"request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results\",\n \"ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The\",\n \"was no error. :type error_code: ErrorCode :param error_message: The result message, or null\",\n \"The election results, or an empty array if the requester did not have\",\n \"self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key:\",\n \"results, or an empty array if the requester did not have permission and\",\n \"List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic: str :param partition_result: The\",\n \"partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message:\",\n \"message, or null if there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id\",\n \"import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id:\",\n \"if the requester did not have permission and the request asks for all\",\n \"..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self,\",\n \"List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results:\",\n \"throttle_time_ms: int :param replica_election_results: The election results, or an empty array if the\",\n \"did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results,\",\n \"error_code: ErrorCode :param error_message: The result message, or null if there was no\",\n \"id :type partition_id: int :param error_code: The result error, or zero if there\",\n \"ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int,\",\n \"import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData\",\n \"replica_election_results: The election results, or an empty array if the requester did not\",\n \"api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms:\",\n \"replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which the request\",\n \":param error_message: The result message, or null if there was no error. :type\",\n \"The result message, or null if there was no error. :type error_message: Optional[str]\",\n \"= partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def\",\n \"for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results = replica_election_results\",\n \"= error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self,\",\n \"or zero if there was no error. :type error_code: ErrorCode :param error_message: The\",\n \"error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int :param error_code:\",\n \"throttled due to a quota violation, or zero if the request did not\",\n \"ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int,\",\n \"or an empty array if the requester did not have permission and the\",\n \"error_message: The result message, or null if there was no error. :type error_message:\",\n \":type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message\",\n \"partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int\",\n \"def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition\",\n \"Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int :param error_code: The\",\n \"topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic: str\",\n \"from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def\",\n \"<gh_stars>0 from typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from\",\n \":param error_code: The result error, or zero if there was no error. :type\",\n \"result error, or zero if there was no error. :type error_code: ErrorCode :param\",\n \"have permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\"\",\n \"partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type\",\n \"if there was no error. :type error_code: ErrorCode :param error_message: The result message,\",\n \"The partition id :type partition_id: int :param error_code: The result error, or zero\",\n \"result message, or null if there was no error. :type error_message: Optional[str] \\\"\\\"\\\"\",\n \"error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\"\",\n \"election results, or an empty array if the requester did not have permission\",\n \"partition id :type partition_id: int :param error_code: The result error, or zero if\",\n \"ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param\",\n \"for which the request was throttled due to a quota violation, or zero\",\n \"no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message\",\n \"request did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election\",\n \"__init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id\",\n \"self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS\",\n \"ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str]\",\n \"in milliseconds for which the request was throttled due to a quota violation,\",\n \"The result error, or zero if there was no error. :type error_code: ErrorCode\",\n \"= ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration\",\n \"any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or an empty\",\n \"for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result\",\n \":param throttle_time_ms: The duration in milliseconds for which the request was throttled due\",\n \"permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms\",\n \"...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code:\",\n \"List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name\",\n \"name :type topic: str :param partition_result: The results for each partition :type partition_result:\",\n \"was throttled due to a quota violation, or zero if the request did\",\n \"str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic: str :param\",\n \"asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results =\",\n \"class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\"\",\n \"not violate any quota. :type throttle_time_ms: int :param replica_election_results: The election results, or\",\n \"quota violation, or zero if the request did not violate any quota. :type\",\n \"if the request did not violate any quota. :type throttle_time_ms: int :param replica_election_results:\",\n \"did not have permission and the request asks for all partitions. :type replica_election_results:\",\n \"The duration in milliseconds for which the request was throttled due to a\",\n \"= partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result:\",\n \"List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which the request was\",\n \"int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which the\",\n \"ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param\",\n \"and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms =\",\n \"requester did not have permission and the request asks for all partitions. :type\",\n \"List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param\",\n \"a quota violation, or zero if the request did not violate any quota.\",\n \"error_code: The result error, or zero if there was no error. :type error_code:\",\n \"= topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey]\",\n \"topic name :type topic: str :param partition_result: The results for each partition :type\",\n \"class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms:\",\n \"__init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for\",\n \"or zero if the request did not violate any quota. :type throttle_time_ms: int\",\n \"\\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int :param error_code: The result\",\n \"int :param error_code: The result error, or zero if there was no error.\",\n \"import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode\",\n \"List, Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult:\",\n \"\\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which the request was throttled\",\n \"each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class\",\n \"self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str\",\n \"\\\"\\\"\\\" :param topic: The topic name :type topic: str :param partition_result: The results\",\n \"partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData):\",\n \":param partition_id: The partition id :type partition_id: int :param error_code: The result error,\",\n \"there was no error. :type error_code: ErrorCode :param error_message: The result message, or\",\n \"error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]):\",\n \"an empty array if the requester did not have permission and the request\",\n \"topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] =\",\n \"was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code\",\n \"error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic:\",\n \"int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]):\",\n \"ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int :param\",\n \"partition_id: int :param error_code: The result error, or zero if there was no\",\n \"there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code =\",\n \"def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds\",\n \"empty array if the requester did not have permission and the request asks\",\n \"int :param replica_election_results: The election results, or an empty array if the requester\",\n \"\\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic:\",\n \"replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\"\",\n \"topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic:\",\n \"error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int\",\n \"not have permission and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult]\",\n \"class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code:\",\n \"self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str,\",\n \"to a quota violation, or zero if the request did not violate any\",\n \"violation, or zero if the request did not violate any quota. :type throttle_time_ms:\",\n \"str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The\",\n \"from typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base\",\n \"= error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result:\",\n \"partition_result: The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic\",\n \"self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def\"\n]","isConversational":false}}},{"rowIdx":3494,"cells":{"ngram":{"kind":"list like","value":["assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy =","log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if","stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert","\"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with","import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all","py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert","\"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage():","test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]):","it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with","== py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs():","== 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get()","[]): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True)","with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with","assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\",","not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get()","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get()","= py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy():","\"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert","the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\",","test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop()","this still works if we don't pass in a custom storage. assert not","# The following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't,","_thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs():","context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def","py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] ==","py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still","sure this still works if we don't pass in a custom storage. assert","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests():","def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with","to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer","stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy","not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log:","py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\"","def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't pass","= stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\",","fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not","a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count ==","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"])","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\",","still works if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get()","assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop()","not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer()","\"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get()","exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer =","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\"","pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all expect","test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not","py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack","def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert","py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\"","scope=\"module\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if","# Let's make sure this still works if we don't pass in a","assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]):","following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will","mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests","def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" ==","py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\"","mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def","if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get()","assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as","with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def","[]): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer =","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert","expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def","1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't","= py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list():","mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []):","assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack =","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" ==","test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def","== context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\"","def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" ==","def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def","stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\",","== stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\")","== py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert","assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer","storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]):","def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get()","test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests():","== stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\")","pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer()","not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with","\"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\")","will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\"","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop()","test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't pass in","assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert","py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\"","[\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert","\"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []):","not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\"","py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\"","log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list():","not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this","create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if it #","with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1","works if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def","the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"] ==","test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get()","assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def","stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\")","\"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\",","don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer =","\"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack =","[\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack","stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack =","\"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy()","all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get()","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get()","\"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop()","== py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert","tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail.","[\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def","assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get()","def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert","test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\",","@pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist:","\"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"])","stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\")","Let's make sure this still works if we don't pass in a custom","stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"]","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\"","autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): #","make sure this still works if we don't pass in a custom storage.","import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs","doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","\"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list():","py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer","py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer","\"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer =","\"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer","context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" ==","stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def","in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not","stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"] == stack._storage","mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs():","== py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer =","assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer()","test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\",","def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if it","\"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack()","if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list():","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" ==","context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\")","py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not","assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not","== py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert","# doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert","def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count","The following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" ==","= py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"]","test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get()","== py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert","py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" ==","== context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" ==","py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to","import mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following","def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get()","custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" ==","\"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs():","mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log,","def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def","assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" =="],"string":"[\n \"assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy =\",\n \"log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if\",\n \"stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert\",\n \"\\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all\",\n \"py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert\",\n \"\\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage():\",\n \"test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]):\",\n \"it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"== py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs():\",\n \"== 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"[]): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True)\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with\",\n \"assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\",\",\n \"not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get()\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"= py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy():\",\n \"\\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert\",\n \"the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\",\",\n \"test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop()\",\n \"this still works if we don't pass in a custom storage. assert not\",\n \"# The following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't,\",\n \"_thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs():\",\n \"context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def\",\n \"py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] ==\",\n \"py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still\",\n \"sure this still works if we don't pass in a custom storage. assert\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests():\",\n \"def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer\",\n \"stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy\",\n \"not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log:\",\n \"py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\"\",\n \"def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't pass\",\n \"= stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\",\",\n \"fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not\",\n \"a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count ==\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"])\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\",\",\n \"still works if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get()\",\n \"assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop()\",\n \"not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"\\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get()\",\n \"exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer =\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\"\",\n \"pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect\",\n \"test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not\",\n \"py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack\",\n \"def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert\",\n \"py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\"\",\n \"scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if\",\n \"# Let's make sure this still works if we don't pass in a\",\n \"assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]):\",\n \"following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will\",\n \"mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests\",\n \"def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" ==\",\n \"py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\"\",\n \"mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def\",\n \"if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get()\",\n \"assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def\",\n \"[]): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer =\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert\",\n \"expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def\",\n \"1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't\",\n \"= py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list():\",\n \"mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []):\",\n \"assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack =\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" ==\",\n \"test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"== context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\"\",\n \"def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" ==\",\n \"def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def\",\n \"stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\",\",\n \"== stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\")\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert\",\n \"assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer\",\n \"storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]):\",\n \"def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests():\",\n \"== stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\")\",\n \"pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"\\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\")\",\n \"will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\"\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop()\",\n \"test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we don't pass in\",\n \"assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\"\",\n \"[\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert\",\n \"\\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []):\",\n \"not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\"\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\"\",\n \"log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list():\",\n \"not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this\",\n \"create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if it #\",\n \"with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1\",\n \"works if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def\",\n \"the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"] ==\",\n \"test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get()\",\n \"assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\")\",\n \"\\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\",\",\n \"don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer =\",\n \"\\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack =\",\n \"[\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack\",\n \"stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack =\",\n \"\\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy()\",\n \"all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get()\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get()\",\n \"\\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop()\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert\",\n \"tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail.\",\n \"[\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def\",\n \"assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get()\",\n \"def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert\",\n \"test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\",\",\n \"@pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist:\",\n \"\\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"])\",\n \"stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\")\",\n \"Let's make sure this still works if we don't pass in a custom\",\n \"stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"]\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\"\",\n \"autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): #\",\n \"make sure this still works if we don't pass in a custom storage.\",\n \"import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs\",\n \"doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"\\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list():\",\n \"py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer\",\n \"py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer\",\n \"\\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer =\",\n \"\\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer\",\n \"context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" ==\",\n \"stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def\",\n \"in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not\",\n \"stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"] == stack._storage\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs():\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer =\",\n \"assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\",\",\n \"def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist: if it\",\n \"\\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack()\",\n \"if we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list():\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" ==\",\n \"context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\")\",\n \"py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not\",\n \"assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not\",\n \"== py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert\",\n \"# doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert\",\n \"def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count\",\n \"The following tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" ==\",\n \"= py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"]\",\n \"test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get()\",\n \"== py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert\",\n \"py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" ==\",\n \"== context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" ==\",\n \"py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to\",\n \"import mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following\",\n \"def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" ==\",\n \"\\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs():\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log,\",\n \"def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" ==\"\n]","isConversational":false}}},{"rowIdx":3495,"cells":{"ngram":{"kind":"list like","value":["AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds","class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)),","= 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds =","store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot(","pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER","= ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img()","= [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please","a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self,","'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted, example: air'),","must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs","variable name to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple","# https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4","output=None): output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig","plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process):","import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS","netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output","plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close()","'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource)","output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example:","... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import","return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS,","ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the","Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice","import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource,","ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns","no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab","= logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run first","self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs,","version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data","from pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging","outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output =","first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from","_handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output:","ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [","variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100) return response","supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name","resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100) return","Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS')","run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs","super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0',","import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server","matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS =","request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\",","plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable =","fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar()","inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output","10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return","output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig =","be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True),","fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset',","= request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file =","LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import","name to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot',","output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10))","https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import","= output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20,","ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output","logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be","plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ]","abstract='Please enter the variable name to be plotted, example: air'), ] outputs =","ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs =","def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced","matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset","to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')],","outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot',","= Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0,","import matplotlib # no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/","Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png'","output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot","LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted, example:","plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def","abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def","'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a","and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response):","cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None,","import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER =","be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as","request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output","= [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple","import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or","] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.',","LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run","= plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output)","from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output =","[ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot',","= simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\",","[ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter","or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax","Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ...","plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def","air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__(","supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and","nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request,","ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines()","# no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import","inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air',","X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as","__init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string',","title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True",":]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs","simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable","default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to","import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must","matplotlib # no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg')","'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable","the variable name to be plotted, example: air'), ] outputs = [ ComplexOutput('output',","self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs,","'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax =","values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :])","] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler,","data_type='string', default='air', abstract='Please enter the variable name to be plotted, example: air'), ]","default='air', abstract='Please enter the variable name to be plotted, example: air'), ] outputs","status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file,","enter the variable name to be plotted, example: air'), ] outputs = [","ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib",":, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self):","response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output)","as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None):","as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple","from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no","= plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class","plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')],","def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable',","identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True,","server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt","example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot,","variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file","SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable',","ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree())","{0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted,","Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :,","simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values =","variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable]","Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True )","plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset',","logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run first #",") def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable)","ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib #","pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11","simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100)","abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be","as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc'","def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values","ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output"],"string":"[\n \"AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds\",\n \"class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)),\",\n \"= 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds =\",\n \"store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot(\",\n \"pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER\",\n \"= ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img()\",\n \"= [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please\",\n \"a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self,\",\n \"'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted, example: air'),\",\n \"must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs\",\n \"variable name to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple\",\n \"# https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4\",\n \"output=None): output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig\",\n \"plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process):\",\n \"import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS\",\n \"netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output\",\n \"plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close()\",\n \"'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource)\",\n \"output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example:\",\n \"... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import\",\n \"return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS,\",\n \"ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the\",\n \"Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice\",\n \"import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource,\",\n \"ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns\",\n \"no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab\",\n \"= logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run first\",\n \"self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs,\",\n \"version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data\",\n \"from pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging\",\n \"outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output =\",\n \"first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from\",\n \"_handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output:\",\n \"ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [\",\n \"variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100) return response\",\n \"supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name\",\n \"resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100) return\",\n \"Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS')\",\n \"run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs\",\n \"super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0',\",\n \"import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server\",\n \"matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS =\",\n \"request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\",\",\n \"plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable =\",\n \"fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar()\",\n \"inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output\",\n \"10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return\",\n \"output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig =\",\n \"be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True),\",\n \"fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset',\",\n \"= request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file =\",\n \"LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import\",\n \"name to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot',\",\n \"output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10))\",\n \"https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import\",\n \"= output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20,\",\n \"ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output\",\n \"logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be\",\n \"plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ]\",\n \"abstract='Please enter the variable name to be plotted, example: air'), ] outputs =\",\n \"ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs =\",\n \"def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced\",\n \"matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import Dataset\",\n \"to be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')],\",\n \"outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot',\",\n \"= Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0,\",\n \"import matplotlib # no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/\",\n \"Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png'\",\n \"output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot\",\n \"LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted, example:\",\n \"plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def\",\n \"abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def\",\n \"'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a\",\n \"and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response):\",\n \"cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None,\",\n \"import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER =\",\n \"be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as\",\n \"request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output\",\n \"= [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple\",\n \"import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or\",\n \"] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.',\",\n \"LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run\",\n \"= plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output)\",\n \"from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output =\",\n \"[ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot',\",\n \"= simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\",\",\n \"[ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter\",\n \"or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax\",\n \"Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ...\",\n \"plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def\",\n \"air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__(\",\n \"supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and\",\n \"nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request,\",\n \"ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines()\",\n \"# no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import\",\n \"inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air',\",\n \"X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as\",\n \"__init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string',\",\n \"title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True\",\n \":]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs\",\n \"simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable\",\n \"default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to\",\n \"import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ... must\",\n \"matplotlib # no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg')\",\n \"'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable\",\n \"the variable name to be plotted, example: air'), ] outputs = [ ComplexOutput('output',\",\n \"self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs,\",\n \"'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax =\",\n \"values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :])\",\n \"] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler,\",\n \"data_type='string', default='air', abstract='Please enter the variable name to be plotted, example: air'), ]\",\n \"default='air', abstract='Please enter the variable name to be plotted, example: air'), ] outputs\",\n \"status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file,\",\n \"enter the variable name to be plotted, example: air'), ] outputs = [\",\n \"ComplexInput, ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib\",\n \":, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self):\",\n \"response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output)\",\n \"as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None):\",\n \"as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple\",\n \"from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no\",\n \"= plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class\",\n \"plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')],\",\n \"def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable',\",\n \"identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True,\",\n \"server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt\",\n \"example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot,\",\n \"variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file\",\n \"SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable',\",\n \"ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree())\",\n \"{0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be plotted,\",\n \"Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :,\",\n \"simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values =\",\n \"variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable]\",\n \"Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True )\",\n \"plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset',\",\n \"logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be run first #\",\n \") def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable)\",\n \"ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib #\",\n \"pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11\",\n \"simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100)\",\n \"abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to be\",\n \"as plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc'\",\n \"def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource) values\",\n \"ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output\"\n]","isConversational":false}}},{"rowIdx":3496,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]","isConversational":false}}},{"rowIdx":3497,"cells":{"ngram":{"kind":"list like","value":["for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def","(%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values =","text = value[0][0] else: text = \"something when wrong\" if value is None:","= db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s \", [correct_answer_id]) if","INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"})","(answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\",","None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values = []","a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id,","room r WHERE r.creator=%s\", [user_id]) response = [] for val in values: response.append({\"id\":","other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0])","room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms')","= value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\",","question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q \"","@app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room","for q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id,","COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id)","= request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id =","id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id = %s","VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values","id, question FROM question WHERE room_id = %s\", [room_id]) response = [] for","AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT","[user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT","> 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0],","get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask =","= request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values =","the mail email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user","= db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id =","from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db","\"What did %s answer to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\":","= db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\", [room_id]) response =","random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions,","r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been opened successfully\", \"status\":","= %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \"","value is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id ==","Room r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question","ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE","json.dumps({\"error\": \"Not available questions for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer():","question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room():","db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id = %s\",","a.answer FROM answer a WHERE a.id = %s \", [correct_answer_id]) if len(value) >","json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404","print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\":","= db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id)","0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user})","r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been closed successfully\", \"status\":","received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started'","import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import","and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0])","FROM room r WHERE r.creator=%s\", [user_id]) response = [] for val in values:","has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE","1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)","= db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id])","AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) >","= value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE","q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q])","from flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import","quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions for","%s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id","%s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value =","%s\", [room_id]) response = [] for val in values: response.append({\"id\": val[0], \"text\": val[1]})","= %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value","0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users)","r.status='finished' WHERE r.id = %s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id)","?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json })","= request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a","questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\":","WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question():","a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email","re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__))","json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id, question)","json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id')","len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions)","in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return","print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\":","WHERE r.id = %s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM","db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The","for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def","json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values = [] for a","room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id","return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values = [] for","for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question'","[] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id =","return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for","@app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE","None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"]","quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE","JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND","COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in values: ranking.append({\"email\": row[0], \"correct\":","%s\", [room_id]) return json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room')","= db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE","json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available","(%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id =","- 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data","to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\":","> 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) >","-*- import json import os.path import random import re from flask import Flask,","is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values =","q \" \"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\",","= int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = []","\" \"FROM answer a INNER JOIN question q \" \"WHERE a.question_id = q.id","= json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s,","asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET","qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2]","db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be the","room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id,","= random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id =","is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room():","user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id =","id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id","q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0})","WHERE 'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE","\"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What","else: text = \"something when wrong\" if value is None: return json.dumps({\"error\": \"Internal","received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id,","\"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if","4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root():","\" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text =","json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} })","import os.path import random import re from flask import Flask, send_from_directory from flask","values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data","quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\",","%s\", [room_id]) return json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room')","user_name = value[0][0] question_text = \"What did %s answer to '%s' ?\" %","answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id'))","return json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room():","@app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id","flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) # Regular expression","def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id =","else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)","# Regular expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers =","random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask =","qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id =","q.question \" \"FROM question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text =","json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q","room r SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room","room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\",","= %s\", [room_id]) response = [] for val in values: response.append({\"id\": val[0], \"text\":","[] for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST'])","JSON found\"}), 404 user_id = json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]:","= db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email =","\"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id = %s AND question_id","if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room')","FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM","JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER","%s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking =","answer a WHERE a.id = %s \", [correct_answer_id]) if len(value) > 0: text","answer will be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id)","To obtain the mail email = request.args.get('email') print(email) if email is None: return","u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text","JOIN question q \" \"WHERE a.question_id = q.id AND q.room_id = %s AND","room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE room_id =","json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values =","certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma:","def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask","json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\":","get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room():","db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0]","request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__)","= %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0:","text_id)) # if commented the first answer will be the correct one random.shuffle(answers)","row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room():","= [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) #","rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP BY","value[0][0] else: text = \"something when wrong\" if value is None: return json.dumps({\"error\":","db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question')","found\"}), 404 user_id = json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"],","random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id,","[asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s answer to '%s' ?\"","q.id = %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM","= [] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking})","= [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id","\"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\":","a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return","\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id])","post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s","to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def","expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7)","\"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if","db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id =","db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\", [room_id]) # for #","only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir():","room r SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room","\"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \"","db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms():","1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"})","join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members","if json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"]","FROM Room WHERE id = 1 values = db.exec_query(\"SELECT status FROM room WHERE","correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a","%s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id')","= %s\", [room_id]) return json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"})","range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if","(creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id')","len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return","question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id","import re from flask import Flask, send_from_directory from flask import request, abort from","< len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers","values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question q \"","SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been","\"INNER JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id","coding: utf-8 -*- import json import os.path import random import re from flask","ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id')","= request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\", [quiz_answer_id,","\"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed'","files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no","asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id))","0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i])","[] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT","WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \"","response = [] for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response)","[] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id)","u.id) \" \"INNER JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id","\" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = []","\"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__ == '__main__': flask_run(app)","db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data","val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json()","\"Not available questions for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id","[user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question","json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id","question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\":","id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator)","JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON","for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id","> 0: text = value[0][0] else: text = \"something when wrong\" if value","= json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id,","return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user =","\" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id","\"FROM question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value","(u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \"","q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE","correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__ == '__main__':","question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE","id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id","possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) >","methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no","room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values","len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0","r SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has","quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in","user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT","quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id =","first answer will be the correct one random.shuffle(answers) answer_json = [] for (answer_id,","None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id","return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not","value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer","when wrong\" if value is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({","quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER","questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES","val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room():","random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id})","INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id","json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions","\" \"WHERE asked_user_id = %s AND about_user_id = %s AND question_id = %s\",","%s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM","0: text = value[0][0] else: text = \"something when wrong\" if value is","db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email')","def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id =","flask_run import datab.social_database as db app = Flask(__name__) # Regular expression to only","db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id})","flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app","= %s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r","if value is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id","'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail email =","json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email)","a.id \" \"FROM answer a INNER JOIN question q \" \"WHERE a.question_id =","id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) >","return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id","else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO","json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id","= \"What did %s answer to '%s' ?\" % (user_name, question_text) return json.dumps({","= db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) >","= request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\",","value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s \",","from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) # Regular","\"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions for this","answer a INNER JOIN question q \" \"WHERE a.question_id = q.id AND q.room_id","qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id","possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id =","= %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value","[user_id]) response = [] for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return","int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id","print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user})","pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename):","= db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id = %s\", [question_id[0]])","@app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room')","1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the","'%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json","json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions","random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id =","[] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else:","import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app =","r SET r.status='finished' WHERE r.id = %s\", [room_id]) # for # SELECT id,","values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id =","response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return","db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if","BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row","status FROM Room WHERE id = 1 values = db.exec_query(\"SELECT status FROM room","= json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values)","> 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\":","app = Flask(__name__) # Regular expression to only accept certain files fileChecker =","val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if","user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id =","this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id =","os.path import random import re from flask import Flask, send_from_directory from flask import","if commented the first answer will be the correct one random.shuffle(answers) answer_json =","db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text","[room_id]) return json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def","user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values)","json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status","AND rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\",","text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question'","# To obtain the mail email = request.args.get('email') print(email) if email is None:","\"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id =","%s AND about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id","get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE","[room_id]) return json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def","id = 1 values = db.exec_query(\"SELECT status FROM room WHERE id = %s\",","\" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email","0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0:","i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id))","= db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\":","\"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id =","\" \"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0]","# SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id","u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in","[] for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers',","return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id,","u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON (u.id =","1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id,","%s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value =","def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id =","= request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\", [room_id]) return","room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id')","= %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id =","q \" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT","= [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions =","Room WHERE id = 1 values = db.exec_query(\"SELECT status FROM room WHERE id","(question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id","json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values","room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room():","is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id,","question FROM question WHERE room_id = %s\", [room_id]) response = [] for val","r SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has","= rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP","= q.id AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\":","= request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question","\"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions","# -*- coding: utf-8 -*- import json import os.path import random import re","response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json()","WHERE room_id = %s\", [room_id]) response = [] for val in values: response.append({\"id\":","return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id'))","[room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id)","successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET","(qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON (u.id = rm.user_id) \"","= value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id","= random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id","WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been closed successfully\",","= random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id =","WHERE r.creator=%s\", [user_id]) response = [] for val in values: response.append({\"id\": val[0], \"status\":","[] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO","available questions for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id =","json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room","user_id = json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"]))","@app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id","found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT","None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\":","(answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM","= qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER BY","[room_id]) response = [] for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return","\" \"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\", [room_id,","answers.append((answer_id, text_id)) # if commented the first answer will be the correct one","datab.social_database as db app = Flask(__name__) # Regular expression to only accept certain","db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room')","be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers:","# SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT","if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id)","will be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in","def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON","the first answer will be the correct one random.shuffle(answers) answer_json = [] for","def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\")","values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN","cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename):","[] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status')","flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run","1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0],","> 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask,","question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions for this user","root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403)","\"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id,","fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the","in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE","\"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value","else: return json.dumps({\"error\": \"Not available questions for this user in this room\"}) @app.route('/post_quiz_answer')","@app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id","request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id])","%s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq","asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0:","json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else:","value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id =","response = [] for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\":","db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0:","if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain","INNER JOIN question q \" \"WHERE a.question_id = q.id AND q.room_id = %s","room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer","@app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id","about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0]","min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))):","json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r","question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question","import json import os.path import random import re from flask import Flask, send_from_directory","\"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data","text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will","[room_id]) ranking = [] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return","Flask(__name__) # Regular expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers","values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response =","answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question():","status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id = 1","r.status FROM room r WHERE r.creator=%s\", [user_id]) response = [] for val in","FROM room WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions')","question_text = \"What did %s answer to '%s' ?\" % (user_name, question_text) return","os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(),","= db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = []","# if commented the first answer will be the correct one random.shuffle(answers) answer_json","a.id = %s \", [correct_answer_id]) if len(value) > 0: text = value[0][0] else:","[room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id =","values = db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id]) return json.dumps({","def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON","email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def","\" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in values:","= value[0][0] question_text = \"What did %s answer to '%s' ?\" % (user_name,","row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') #","\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email \"","AND about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id =","abort from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) #","SET answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT","FROM question WHERE room_id = %s\", [room_id]) response = [] for val in","request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values = db.exec_query(\"SELECT","def register(): # To obtain the mail email = request.args.get('email') print(email) if email","successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET","db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if","correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1,","status FROM room WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] })","room WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def","question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value =","= db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users","= db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question q \" \"WHERE","BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in values: ranking.append({\"email\": row[0],","def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE","values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id =","user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id')","if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and","= db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def","quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers","= int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id)","[quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT","> 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions)","request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\", [room_id])","return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values =","user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions)","correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id,","value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id","\"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values = [] for a in","room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\", [room_id])","json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id =","question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id =","value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND","\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id = %s\", [user_id,","open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id =","utf-8 -*- import json import os.path import random import re from flask import",", COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id","0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)","db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value","import flask_run import datab.social_database as db app = Flask(__name__) # Regular expression to","user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user","\"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for","did %s answer to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id,","- 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented","import random import re from flask import Flask, send_from_directory from flask import request,","answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id)","= request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\", [room_id])","\" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id = %s AND","\" \"FROM question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0]","= \"something when wrong\" if value is None: return json.dumps({\"error\": \"Internal server error\"})","answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM","= db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id,","(room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id","}) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM","0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id,","= [] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)","value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id","= request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id","email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email)","@app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\":","request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id])","request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER","json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT","len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users","def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id","VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id')","r.creator=%s\", [user_id]) response = [] for val in values: response.append({\"id\": val[0], \"status\": val[1]})","db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) >","room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id =","user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id =","if json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"]","db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users u","db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id)","r WHERE r.creator=%s\", [user_id]) response = [] for val in values: response.append({\"id\": val[0],","asked_user_id = %s AND about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0],","id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email","json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer)","request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id =","= 1 values = db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id])","values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\",","db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The","if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1,","[question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u \"","val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is","return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM","[room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values","return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id =","random import re from flask import Flask, send_from_directory from flask import request, abort","@app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id)","answer to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text,","print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT","q.id AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values)","= %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id')","Regular expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4","\"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT","= value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM","\"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id","users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did","close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\",","else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if","room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM","\" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email","= %s AND about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]])","json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room","(answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer","commented the first answer will be the correct one random.shuffle(answers) answer_json = []","SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email","users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON (u.id","INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def","qq \" \"INNER JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN","question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room')","request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\", [room_id]) # for","values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data =","if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id","filename) abort(403) @app.route('/register') def register(): # To obtain the mail email = request.args.get('email')","quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE","as db app = Flask(__name__) # Regular expression to only accept certain files","SET r.status='finished' WHERE r.id = %s\", [room_id]) # for # SELECT id, COUNT(a.id),","= request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id","user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id')","return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text}","return json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room():","response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data =","the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\":","def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def","user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN","server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id,","def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename)","index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To","def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id =","question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0],","SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers -","@app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES","possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask)","answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3","FROM answer a WHERE a.id = %s \", [correct_answer_id]) if len(value) > 0:","[room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN","id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email ,","\" \"FROM quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id = u.id)","COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \"","quiz_question \" \"WHERE asked_user_id = %s AND about_user_id = %s AND question_id =","\"FROM quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id = u.id) \"","in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers():","= db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id]) return json.dumps({ \"status\":","= 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def","= Flask(__name__) # Regular expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\")","return json.dumps({\"error\": \"Not available questions for this user in this room\"}) @app.route('/post_quiz_answer') def","a WHERE a.id = %s \", [correct_answer_id]) if len(value) > 0: text =","email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id","has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE","[quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \"","%s \", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text =","WHERE a.id = %s \", [correct_answer_id]) if len(value) > 0: text = value[0][0]","\"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET","%s answer to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\":","return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id,","@app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register():","room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id,","= request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\", [room_id]) #","= db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response = []","= db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be","quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = []","value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s \", [correct_answer_id])","\" \"INNER JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members","random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return","numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/')","index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def","return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values","values = db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\", [room_id]) response","json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\",","request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES","a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return","\"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished'","\"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions =","answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id,","in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data","db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s \", [correct_answer_id]) if len(value)","> 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users =","request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id =","register(): # To obtain the mail email = request.args.get('email') print(email) if email is","in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id","ranking = [] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\":","user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\",","answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' =","re from flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun","if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0])","= %s \", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text","= db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id","value = db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name","len(value) > 0: text = value[0][0] else: text = \"something when wrong\" if","text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value =","\"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id =","rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id =","# SELECT status FROM Room WHERE id = 1 values = db.exec_query(\"SELECT status","@app.route('/register') def register(): # To obtain the mail email = request.args.get('email') print(email) if","[answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i","request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\":","return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register')","WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been opened successfully\",","'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id","def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \"","0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id):","1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id)","-*- coding: utf-8 -*- import json import os.path import random import re from","int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id","r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been opened","'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \"","\"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s answer to","\"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE","u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s answer to '%s'","create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id])","request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question q","%s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id')","user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return","@app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE","return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO","request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id =","JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions:","user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0","qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id])","post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}),","SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id \"","closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r","accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): #","= request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id,","get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE room_id","json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None:","= %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question","= %s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking","answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE","asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) =","(room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def","= value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s","% (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else:","= request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\":","request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\":","wrong\" if value is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\":","len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if","len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers =","answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers","methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no","user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return","ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON (u.id = rm.user_id)","text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers -","}) else: return json.dumps({\"error\": \"Not available questions for this user in this room\"})","if len(value) > 0: text = value[0][0] else: text = \"something when wrong\"","db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0]","= u.id) \" \"INNER JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE","= value[0][0] else: text = \"something when wrong\" if value is None: return","asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id","text = \"something when wrong\" if value is None: return json.dumps({\"error\": \"Internal server","FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question","answer_json }) else: return json.dumps({\"error\": \"Not available questions for this user in this","root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>')","= re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return","return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room","[room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email')","qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id)","row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status","this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question","a INNER JOIN question q \" \"WHERE a.question_id = q.id AND q.room_id =","been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room","VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id","random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s","no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if","@app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'),","0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) >","text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id,","val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data","user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id =","answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id'","value = db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id = %s\",","in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) #","answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer","is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions =","DESC\", [room_id]) ranking = [] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]})","= request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id","SELECT status FROM Room WHERE id = 1 values = db.exec_query(\"SELECT status FROM","if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if","request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\", [room_id]) return","= random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask","INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def","\"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions for this user in","= db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name =","db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response = [] for","answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__","values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1])","\" \"INNER JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id =","for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST'])","# for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values","@app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\":","return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return","in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE","%s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u","def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO","values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question","json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) -","question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions,","value[0][0] question_text = \"What did %s answer to '%s' ?\" % (user_name, question_text)","= db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0:","request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response","for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values =","= [] for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response})","VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id","db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question","def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): #","question q \" \"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id=","= request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id)","id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM","finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\",","send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail email","json import os.path import random import re from flask import Flask, send_from_directory from","room_id = %s\", [room_id]) response = [] for val in values: response.append({\"id\": val[0],","db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id","request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id')","= request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id])","1 values = db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id]) return","def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\",","1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask,","room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question","@app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question","\"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in values: ranking.append({\"email\":","\"FROM answer a INNER JOIN question q \" \"WHERE a.question_id = q.id AND","qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1]","u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users u ON","%s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for","return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None:","= %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users","(%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id","rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id])","len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first","= %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id,","AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id():","qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER","Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database","@app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id","\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text = \"something","\" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s answer","= [] for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room',","fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"})","for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer","%s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email =","r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq","for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id,","obtain the mail email = request.args.get('email') print(email) if email is None: return json.dumps({})","quiz_question SET answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value =","INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \"","\"INNER JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm","question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else:","%s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def","\"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE","%s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER","= %s\", [room_id]) return json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"})","return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is","abort(403) @app.route('/register') def register(): # To obtain the mail email = request.args.get('email') print(email)","in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id,","error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\":","SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been","r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been closed","asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q","\"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in","id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email)","request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id})","= request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return","len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id =","== correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__ ==","WHERE id = 1 values = db.exec_query(\"SELECT status FROM room WHERE id =","(user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return","= 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id =","question WHERE room_id = %s\", [room_id]) response = [] for val in values:","> 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 <","possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if","[correct_answer_id]) if len(value) > 0: text = value[0][0] else: text = \"something when","ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s","questions for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id')","send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as","quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE","len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) =","COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id =","json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return","value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]])","db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id,","def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r","db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT","one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\":","= db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id =","asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions","= [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT","# pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/<path:filename>') def","'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM","room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET","db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\", [room_id]) response = []","r.id = %s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room","mail email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user =","fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover","r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response = [] for val","3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s","room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\", [room_id])","WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users))","room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id')","db app = Flask(__name__) # Regular expression to only accept certain files fileChecker","room r SET r.status='finished' WHERE r.id = %s\", [room_id]) # for # SELECT","return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail","val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def","import datab.social_database as db app = Flask(__name__) # Regular expression to only accept","db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question q \" \"WHERE a.question_id","u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s","opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r","\"something when wrong\" if value is None: return json.dumps({\"error\": \"Internal server error\"}) return","- 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id,","been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room","other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be the correct","404 user_id = json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id,","email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\","],"string":"[\n \"for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def\",\n \"(%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values =\",\n \"text = value[0][0] else: text = \\\"something when wrong\\\" if value is None:\",\n \"= db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s \\\", [correct_answer_id]) if\",\n \"INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"})\",\n \"(answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\",\",\n \"None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = []\",\n \"a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id,\",\n \"room r WHERE r.creator=%s\\\", [user_id]) response = [] for val in values: response.append({\\\"id\\\":\",\n \"other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0])\",\n \"room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms')\",\n \"= value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\",\",\n \"question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\"\",\n \"@app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room\",\n \"for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id,\",\n \"COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id)\",\n \"= request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id =\",\n \"id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s\",\n \"VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values\",\n \"id, question FROM question WHERE room_id = %s\\\", [room_id]) response = [] for\",\n \"AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT\",\n \"[user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT\",\n \"> 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0],\",\n \"get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask =\",\n \"= request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values =\",\n \"the mail email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user\",\n \"= db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id =\",\n \"from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db\",\n \"\\\"What did %s answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\":\",\n \"= db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\", [room_id]) response =\",\n \"random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions,\",\n \"r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\":\",\n \"= %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\"\",\n \"value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id ==\",\n \"Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question\",\n \"ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE\",\n \"json.dumps({\\\"error\\\": \\\"Not available questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer():\",\n \"question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room():\",\n \"db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\",\",\n \"a.answer FROM answer a WHERE a.id = %s \\\", [correct_answer_id]) if len(value) >\",\n \"json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404\",\n \"print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\":\",\n \"= db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id)\",\n \"0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user})\",\n \"r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\":\",\n \"received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started'\",\n \"import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import\",\n \"and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0])\",\n \"FROM room r WHERE r.creator=%s\\\", [user_id]) response = [] for val in values:\",\n \"has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE\",\n \"1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)\",\n \"= db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id])\",\n \"AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) >\",\n \"= value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE\",\n \"q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q])\",\n \"from flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import\",\n \"quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for\",\n \"%s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id\",\n \"%s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value =\",\n \"%s\\\", [room_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]})\",\n \"= %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value\",\n \"0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users)\",\n \"r.status='finished' WHERE r.id = %s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id)\",\n \"?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json })\",\n \"= request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a\",\n \"questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\":\",\n \"WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question():\",\n \"a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email\",\n \"re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__))\",\n \"json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question)\",\n \"json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id')\",\n \"len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions)\",\n \"in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return\",\n \"print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\":\",\n \"WHERE r.id = %s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The\",\n \"for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def\",\n \"json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for a\",\n \"room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id\",\n \"return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for\",\n \"for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question'\",\n \"[] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id =\",\n \"return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for\",\n \"@app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE\",\n \"None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"]\",\n \"quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE\",\n \"JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND\",\n \"COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\":\",\n \"%s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room')\",\n \"= db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE\",\n \"json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available\",\n \"(%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id =\",\n \"- 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data\",\n \"to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\":\",\n \"> 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) >\",\n \"-*- import json import os.path import random import re from flask import Flask,\",\n \"is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values =\",\n \"q \\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\\\",\",\n \"= int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = []\",\n \"\\\" \\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id\",\n \"= json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s,\",\n \"asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET\",\n \"qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2]\",\n \"db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be the\",\n \"room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id,\",\n \"= random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id =\",\n \"is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room():\",\n \"user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id =\",\n \"id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id\",\n \"q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0})\",\n \"WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE\",\n \"\\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What\",\n \"else: text = \\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal\",\n \"received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id,\",\n \"\\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if\",\n \"4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root():\",\n \"\\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text =\",\n \"json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} })\",\n \"import os.path import random import re from flask import Flask, send_from_directory from flask\",\n \"values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data\",\n \"quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\\\",\",\n \"%s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room')\",\n \"user_name = value[0][0] question_text = \\\"What did %s answer to '%s' ?\\\" %\",\n \"answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id'))\",\n \"return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room():\",\n \"@app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id\",\n \"flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) # Regular expression\",\n \"def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id =\",\n \"else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)\",\n \"# Regular expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers =\",\n \"random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask =\",\n \"qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id =\",\n \"q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text =\",\n \"json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q\",\n \"room r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room\",\n \"room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\",\",\n \"= %s\\\", [room_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\":\",\n \"[] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST'])\",\n \"JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]:\",\n \"= db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email =\",\n \"\\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id\",\n \"if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room')\",\n \"FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM\",\n \"JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER\",\n \"%s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking =\",\n \"answer a WHERE a.id = %s \\\", [correct_answer_id]) if len(value) > 0: text\",\n \"answer will be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id)\",\n \"To obtain the mail email = request.args.get('email') print(email) if email is None: return\",\n \"u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text\",\n \"JOIN question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s AND\",\n \"room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id =\",\n \"json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values =\",\n \"certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma:\",\n \"def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask\",\n \"json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\":\",\n \"get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room():\",\n \"db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0]\",\n \"request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__)\",\n \"= %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0:\",\n \"text_id)) # if commented the first answer will be the correct one random.shuffle(answers)\",\n \"row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room():\",\n \"= [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) #\",\n \"rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY\",\n \"value[0][0] else: text = \\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\":\",\n \"db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question')\",\n \"found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"],\",\n \"random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id,\",\n \"[asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s answer to '%s' ?\\\"\",\n \"q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM\",\n \"= [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking})\",\n \"= [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id\",\n \"\\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\":\",\n \"a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return\",\n \"\\\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id])\",\n \"post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s\",\n \"to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def\",\n \"expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7)\",\n \"\\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if\",\n \"db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id =\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for #\",\n \"only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir():\",\n \"room r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room\",\n \"\\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\"\",\n \"db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms():\",\n \"1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"})\",\n \"join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members\",\n \"if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"]\",\n \"FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM room WHERE\",\n \"correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a\",\n \"%s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id')\",\n \"= %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"})\",\n \"range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if\",\n \"(creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id')\",\n \"len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return\",\n \"question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id\",\n \"import re from flask import Flask, send_from_directory from flask import request, abort from\",\n \"< len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers\",\n \"values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question q \\\"\",\n \"SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been\",\n \"\\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id\",\n \"coding: utf-8 -*- import json import os.path import random import re from flask\",\n \"ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id')\",\n \"= request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id,\",\n \"\\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed'\",\n \"files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no\",\n \"asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id))\",\n \"0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i])\",\n \"[] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT\",\n \"WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\"\",\n \"response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response)\",\n \"[] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id)\",\n \"u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id\",\n \"\\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = []\",\n \"\\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ == '__main__': flask_run(app)\",\n \"db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data\",\n \"val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json()\",\n \"\\\"Not available questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id\",\n \"[user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question\",\n \"json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id\",\n \"question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\":\",\n \"id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator)\",\n \"JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON\",\n \"for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id\",\n \"> 0: text = value[0][0] else: text = \\\"something when wrong\\\" if value\",\n \"= json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id,\",\n \"return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user =\",\n \"\\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id\",\n \"\\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value\",\n \"(u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\"\",\n \"q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE\",\n \"correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ == '__main__':\",\n \"question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE\",\n \"id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id\",\n \"possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) >\",\n \"methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no\",\n \"room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values\",\n \"len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0\",\n \"r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has\",\n \"quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in\",\n \"user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT\",\n \"quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id =\",\n \"first answer will be the correct one random.shuffle(answers) answer_json = [] for (answer_id,\",\n \"None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id\",\n \"return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not\",\n \"value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer\",\n \"when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({\",\n \"quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER\",\n \"questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES\",\n \"val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room():\",\n \"random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id})\",\n \"INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id\",\n \"json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions\",\n \"\\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id = %s\\\",\",\n \"%s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM\",\n \"0: text = value[0][0] else: text = \\\"something when wrong\\\" if value is\",\n \"db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email')\",\n \"def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id =\",\n \"flask_run import datab.social_database as db app = Flask(__name__) # Regular expression to only\",\n \"db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id})\",\n \"flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app\",\n \"= %s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r\",\n \"if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id\",\n \"'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail email =\",\n \"json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email)\",\n \"a.id \\\" \\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE a.question_id =\",\n \"id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) >\",\n \"return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id\",\n \"else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO\",\n \"json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id\",\n \"= \\\"What did %s answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({\",\n \"= db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) >\",\n \"= request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\",\",\n \"value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s \\\",\",\n \"from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) # Regular\",\n \"\\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this\",\n \"answer a INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id\",\n \"qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id\",\n \"possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id =\",\n \"= %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value\",\n \"[user_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return\",\n \"int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id\",\n \"print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user})\",\n \"pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename):\",\n \"= db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]])\",\n \"@app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room')\",\n \"1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the\",\n \"'%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json\",\n \"json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions\",\n \"random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id =\",\n \"[] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else:\",\n \"import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db app =\",\n \"r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for # SELECT id,\",\n \"values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id =\",\n \"response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return\",\n \"db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if\",\n \"BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row\",\n \"status FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM room\",\n \"= json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values)\",\n \"> 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\":\",\n \"app = Flask(__name__) # Regular expression to only accept certain files fileChecker =\",\n \"val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if\",\n \"user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id =\",\n \"this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id =\",\n \"os.path import random import re from flask import Flask, send_from_directory from flask import\",\n \"if commented the first answer will be the correct one random.shuffle(answers) answer_json =\",\n \"db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text\",\n \"[room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def\",\n \"user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values)\",\n \"json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status\",\n \"AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\",\",\n \"text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question'\",\n \"# To obtain the mail email = request.args.get('email') print(email) if email is None:\",\n \"\\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id =\",\n \"%s AND about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id\",\n \"get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE\",\n \"[room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def\",\n \"id = 1 values = db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\",\",\n \"\\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email\",\n \"0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0:\",\n \"i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id))\",\n \"= db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\":\",\n \"\\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id =\",\n \"\\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0]\",\n \"# SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id\",\n \"u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in\",\n \"[] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers',\",\n \"return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id,\",\n \"u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id =\",\n \"1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id,\",\n \"%s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value =\",\n \"def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id =\",\n \"= request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return\",\n \"room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id')\",\n \"= %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id =\",\n \"q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT\",\n \"= [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions =\",\n \"Room WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM room WHERE id\",\n \"(question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id\",\n \"json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values\",\n \"room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room():\",\n \"is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id,\",\n \"question FROM question WHERE room_id = %s\\\", [room_id]) response = [] for val\",\n \"r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has\",\n \"= rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP\",\n \"= q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\":\",\n \"= request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question\",\n \"\\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions\",\n \"# -*- coding: utf-8 -*- import json import os.path import random import re\",\n \"response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json()\",\n \"WHERE room_id = %s\\\", [room_id]) response = [] for val in values: response.append({\\\"id\\\":\",\n \"return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id'))\",\n \"[room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id)\",\n \"successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET\",\n \"(qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\"\",\n \"= value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id\",\n \"= random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id\",\n \"WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\",\",\n \"= random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id =\",\n \"WHERE r.creator=%s\\\", [user_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\":\",\n \"[] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO\",\n \"available questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id =\",\n \"json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room\",\n \"user_id = json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"]))\",\n \"@app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id\",\n \"found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT\",\n \"None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\":\",\n \"(answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM\",\n \"= qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY\",\n \"[room_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return\",\n \"\\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id,\",\n \"answers.append((answer_id, text_id)) # if commented the first answer will be the correct one\",\n \"datab.social_database as db app = Flask(__name__) # Regular expression to only accept certain\",\n \"db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room')\",\n \"be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers:\",\n \"# SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT\",\n \"if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id)\",\n \"will be the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in\",\n \"def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON\",\n \"the first answer will be the correct one random.shuffle(answers) answer_json = [] for\",\n \"def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\")\",\n \"values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN\",\n \"cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename):\",\n \"[] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status')\",\n \"flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run\",\n \"1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0],\",\n \"> 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask,\",\n \"question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user\",\n \"root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403)\",\n \"\\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id,\",\n \"fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the\",\n \"in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE\",\n \"\\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value\",\n \"else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user in this room\\\"}) @app.route('/post_quiz_answer')\",\n \"@app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id\",\n \"request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id])\",\n \"%s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq\",\n \"asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0:\",\n \"json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else:\",\n \"value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id =\",\n \"response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\":\",\n \"db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0:\",\n \"if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain\",\n \"INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s\",\n \"room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer\",\n \"@app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id\",\n \"about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0]\",\n \"min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))):\",\n \"json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r\",\n \"question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question\",\n \"import json import os.path import random import re from flask import Flask, send_from_directory\",\n \"\\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data\",\n \"text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will\",\n \"[room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return\",\n \"Flask(__name__) # Regular expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers\",\n \"values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response =\",\n \"answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question():\",\n \"status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id = 1\",\n \"r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response = [] for val in\",\n \"FROM room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions')\",\n \"question_text = \\\"What did %s answer to '%s' ?\\\" % (user_name, question_text) return\",\n \"os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(),\",\n \"= db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = []\",\n \"# if commented the first answer will be the correct one random.shuffle(answers) answer_json\",\n \"a.id = %s \\\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else:\",\n \"[room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id =\",\n \"values = db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id]) return json.dumps({\",\n \"def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON\",\n \"email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def\",\n \"\\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in values:\",\n \"= value[0][0] question_text = \\\"What did %s answer to '%s' ?\\\" % (user_name,\",\n \"row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') #\",\n \"\\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\"\",\n \"AND about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id =\",\n \"abort from flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__) #\",\n \"SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT\",\n \"FROM question WHERE room_id = %s\\\", [room_id]) response = [] for val in\",\n \"request.args.get('room_id') # SELECT status FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT\",\n \"def register(): # To obtain the mail email = request.args.get('email') print(email) if email\",\n \"successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET\",\n \"db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if\",\n \"correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1,\",\n \"status FROM room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] })\",\n \"room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def\",\n \"question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value =\",\n \"= db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users\",\n \"= db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE\",\n \"BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\": row[0],\",\n \"def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE\",\n \"values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id =\",\n \"user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id')\",\n \"if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and\",\n \"= db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def\",\n \"quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers\",\n \"= int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id)\",\n \"[quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT\",\n \"> 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions)\",\n \"request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\", [room_id])\",\n \"return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values =\",\n \"user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions)\",\n \"correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id,\",\n \"value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id\",\n \"\\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for a in\",\n \"room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\", [room_id])\",\n \"json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id =\",\n \"question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id =\",\n \"value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND\",\n \"\\\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id = %s\\\", [user_id,\",\n \"open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id =\",\n \"utf-8 -*- import json import os.path import random import re from flask import\",\n \", COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id\",\n \"0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)\",\n \"db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value\",\n \"import flask_run import datab.social_database as db app = Flask(__name__) # Regular expression to\",\n \"user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user\",\n \"\\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for\",\n \"did %s answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id,\",\n \"- 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented\",\n \"import random import re from flask import Flask, send_from_directory from flask import request,\",\n \"answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id)\",\n \"= request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\", [room_id])\",\n \"\\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s AND\",\n \"\\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0]\",\n \"= \\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"})\",\n \"answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM\",\n \"= db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id,\",\n \"(room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id\",\n \"}) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM\",\n \"0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id,\",\n \"= [] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)\",\n \"value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id\",\n \"= request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id\",\n \"email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email)\",\n \"@app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\":\",\n \"request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id])\",\n \"request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER\",\n \"json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT\",\n \"len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users\",\n \"def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id\",\n \"VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id')\",\n \"r.creator=%s\\\", [user_id]) response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]})\",\n \"db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) >\",\n \"room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id =\",\n \"user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id =\",\n \"if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"]\",\n \"db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u\",\n \"db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id)\",\n \"r WHERE r.creator=%s\\\", [user_id]) response = [] for val in values: response.append({\\\"id\\\": val[0],\",\n \"asked_user_id = %s AND about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0],\",\n \"id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email\",\n \"json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer)\",\n \"request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id =\",\n \"= 1 values = db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id])\",\n \"values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\",\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The\",\n \"if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1,\",\n \"[question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\"\",\n \"val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is\",\n \"return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM\",\n \"[room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values\",\n \"return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id =\",\n \"random import re from flask import Flask, send_from_directory from flask import request, abort\",\n \"@app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id)\",\n \"answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text,\",\n \"print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT\",\n \"q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values)\",\n \"= %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id')\",\n \"Regular expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4\",\n \"\\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT\",\n \"= value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM\",\n \"\\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id\",\n \"users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did\",\n \"close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\",\",\n \"else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if\",\n \"room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM\",\n \"\\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email\",\n \"= %s AND about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]])\",\n \"json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room\",\n \"(answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer\",\n \"commented the first answer will be the correct one random.shuffle(answers) answer_json = []\",\n \"SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email\",\n \"users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id\",\n \"INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def\",\n \"qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN\",\n \"question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room')\",\n \"request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for\",\n \"values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data =\",\n \"if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id\",\n \"filename) abort(403) @app.route('/register') def register(): # To obtain the mail email = request.args.get('email')\",\n \"quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE\",\n \"as db app = Flask(__name__) # Regular expression to only accept certain files\",\n \"SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for # SELECT id, COUNT(a.id),\",\n \"= request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id\",\n \"user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id')\",\n \"return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text}\",\n \"return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room():\",\n \"response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data =\",\n \"the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\":\",\n \"def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def\",\n \"user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN\",\n \"server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id,\",\n \"def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename)\",\n \"index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To\",\n \"def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id =\",\n \"question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0],\",\n \"SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers -\",\n \"@app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES\",\n \"possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask)\",\n \"answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3\",\n \"FROM answer a WHERE a.id = %s \\\", [correct_answer_id]) if len(value) > 0:\",\n \"[room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN\",\n \"id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email ,\",\n \"\\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id)\",\n \"COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\"\",\n \"quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id =\",\n \"\\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\"\",\n \"in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers():\",\n \"= db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\":\",\n \"= 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def\",\n \"= Flask(__name__) # Regular expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\")\",\n \"return json.dumps({\\\"error\\\": \\\"Not available questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def\",\n \"a WHERE a.id = %s \\\", [correct_answer_id]) if len(value) > 0: text =\",\n \"email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id\",\n \"has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE\",\n \"[quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\"\",\n \"%s \\\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text =\",\n \"WHERE a.id = %s \\\", [correct_answer_id]) if len(value) > 0: text = value[0][0]\",\n \"\\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET\",\n \"%s answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\":\",\n \"return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id,\",\n \"@app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register():\",\n \"room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id,\",\n \"= request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) #\",\n \"= db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response = []\",\n \"= db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be\",\n \"quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = []\",\n \"value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s \\\", [correct_answer_id])\",\n \"\\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members\",\n \"random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return\",\n \"numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/')\",\n \"index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def\",\n \"return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values\",\n \"values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\", [room_id]) response\",\n \"json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\",\",\n \"request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES\",\n \"a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return\",\n \"\\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished'\",\n \"\\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions =\",\n \"answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id,\",\n \"in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data\",\n \"db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s \\\", [correct_answer_id]) if len(value)\",\n \"> 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users =\",\n \"request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id =\",\n \"register(): # To obtain the mail email = request.args.get('email') print(email) if email is\",\n \"in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id\",\n \"ranking = [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\":\",\n \"user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\",\",\n \"answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' =\",\n \"re from flask import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun\",\n \"if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0])\",\n \"= %s \\\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text\",\n \"= db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id\",\n \"value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name\",\n \"len(value) > 0: text = value[0][0] else: text = \\\"something when wrong\\\" if\",\n \"text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3 value =\",\n \"\\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id =\",\n \"rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id =\",\n \"# SELECT status FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT status\",\n \"@app.route('/register') def register(): # To obtain the mail email = request.args.get('email') print(email) if\",\n \"[answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i\",\n \"request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\":\",\n \"return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register')\",\n \"WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\",\",\n \"'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id\",\n \"def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\"\",\n \"0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id):\",\n \"1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id)\",\n \"-*- coding: utf-8 -*- import json import os.path import random import re from\",\n \"int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id\",\n \"r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened\",\n \"'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\"\",\n \"\\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s answer to\",\n \"\\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE\",\n \"u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s answer to '%s'\",\n \"create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id])\",\n \"request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question q\",\n \"%s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id')\",\n \"user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return\",\n \"@app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE\",\n \"return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO\",\n \"request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id =\",\n \"JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions:\",\n \"user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0\",\n \"qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id])\",\n \"post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}),\",\n \"SELECT 'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\"\",\n \"closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r\",\n \"accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): #\",\n \"= request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id,\",\n \"get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id\",\n \"json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None:\",\n \"= %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question\",\n \"= %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking\",\n \"answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE\",\n \"asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) =\",\n \"(room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def\",\n \"= value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s\",\n \"% (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else:\",\n \"= request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\":\",\n \"request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\":\",\n \"wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\":\",\n \"len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if\",\n \"len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers =\",\n \"answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers\",\n \"methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no\",\n \"user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return\",\n \"ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id)\",\n \"text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers -\",\n \"}) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user in this room\\\"})\",\n \"if len(value) > 0: text = value[0][0] else: text = \\\"something when wrong\\\"\",\n \"db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0]\",\n \"= u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE\",\n \"= value[0][0] else: text = \\\"something when wrong\\\" if value is None: return\",\n \"asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id\",\n \"text = \\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server\",\n \"FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question\",\n \"answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user in this\",\n \"root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>')\",\n \"= re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover return\",\n \"return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room\",\n \"[room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email')\",\n \"qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id)\",\n \"row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status\",\n \"this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question\",\n \"a INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id =\",\n \"been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room\",\n \"VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id\",\n \"random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s\",\n \"no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if\",\n \"@app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'),\",\n \"0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) >\",\n \"text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id,\",\n \"val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data\",\n \"user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id =\",\n \"answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id'\",\n \"value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\",\",\n \"in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) #\",\n \"answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer\",\n \"is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions =\",\n \"DESC\\\", [room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]})\",\n \"= request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id\",\n \"SELECT status FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM\",\n \"if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if\",\n \"request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return\",\n \"= random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask\",\n \"INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def\",\n \"\\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user in\",\n \"= db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name =\",\n \"db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response = [] for\",\n \"answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__\",\n \"values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1])\",\n \"\\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id =\",\n \"for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST'])\",\n \"# for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values\",\n \"@app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\":\",\n \"return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def index(filename): if fileChecker.match(filename): return\",\n \"in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE\",\n \"%s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u\",\n \"def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO\",\n \"values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question\",\n \"json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) -\",\n \"question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions,\",\n \"value[0][0] question_text = \\\"What did %s answer to '%s' ?\\\" % (user_name, question_text)\",\n \"= db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0:\",\n \"request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response\",\n \"for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values =\",\n \"= [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response})\",\n \"VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id\",\n \"db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question\",\n \"def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): #\",\n \"question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id=\",\n \"= request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id)\",\n \"id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM\",\n \"finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\",\",\n \"send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail email\",\n \"json import os.path import random import re from flask import Flask, send_from_directory from\",\n \"room_id = %s\\\", [room_id]) response = [] for val in values: response.append({\\\"id\\\": val[0],\",\n \"db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions) > 0: question_id\",\n \"request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id')\",\n \"= request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id])\",\n \"1 values = db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id]) return\",\n \"def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\",\",\n \"1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask,\",\n \"room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question\",\n \"@app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question\",\n \"\\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\":\",\n \"\\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id AND\",\n \"qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1]\",\n \"u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON\",\n \"%s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for\",\n \"return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None:\",\n \"= %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users\",\n \"(%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id\",\n \"rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id])\",\n \"len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first\",\n \"= %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id,\",\n \"AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id():\",\n \"qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER\",\n \"Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database\",\n \"@app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id\",\n \"\\\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else: text = \\\"something\",\n \"\\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s answer\",\n \"= [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room',\",\n \"fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"})\",\n \"for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer\",\n \"%s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email =\",\n \"r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq\",\n \"for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id,\",\n \"obtain the mail email = request.args.get('email') print(email) if email is None: return json.dumps({})\",\n \"quiz_question SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value =\",\n \"INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\"\",\n \"\\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm\",\n \"question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else:\",\n \"%s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def\",\n \"\\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE\",\n \"%s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER\",\n \"= %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"})\",\n \"return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is\",\n \"abort(403) @app.route('/register') def register(): # To obtain the mail email = request.args.get('email') print(email)\",\n \"in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id,\",\n \"error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\":\",\n \"SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been\",\n \"r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed\",\n \"asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q\",\n \"\\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in\",\n \"id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email)\",\n \"request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id})\",\n \"= request.args.get('email') print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return\",\n \"len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id =\",\n \"== correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ ==\",\n \"WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM room WHERE id =\",\n \"(user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return\",\n \"= 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id =\",\n \"question WHERE room_id = %s\\\", [room_id]) response = [] for val in values:\",\n \"> 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 <\",\n \"possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if\",\n \"[correct_answer_id]) if len(value) > 0: text = value[0][0] else: text = \\\"something when\",\n \"ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s\",\n \"questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id')\",\n \"send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as\",\n \"quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE\",\n \"len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) =\",\n \"COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id =\",\n \"json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None: return\",\n \"value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]])\",\n \"db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id,\",\n \"def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r\",\n \"db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT\",\n \"one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\":\",\n \"= db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id =\",\n \"asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions\",\n \"= [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT\",\n \"# pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/<path:filename>') def\",\n \"'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM\",\n \"room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET\",\n \"db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\", [room_id]) response = []\",\n \"r.id = %s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room\",\n \"mail email = request.args.get('email') print(email) if email is None: return json.dumps({}) id_user =\",\n \"fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover\",\n \"r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response = [] for val\",\n \"3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s\",\n \"room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\", [room_id])\",\n \"WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users))\",\n \"room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id')\",\n \"db app = Flask(__name__) # Regular expression to only accept certain files fileChecker\",\n \"room r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for # SELECT\",\n \"return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the mail\",\n \"val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def\",\n \"import datab.social_database as db app = Flask(__name__) # Regular expression to only accept\",\n \"db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE a.question_id\",\n \"u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s\",\n \"opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r\",\n \"\\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return\",\n \"- 1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id,\",\n \"been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room\",\n \"other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will be the correct\",\n \"404 user_id = json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id,\",\n \"email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\",\"\n]","isConversational":false}}},{"rowIdx":3498,"cells":{"ngram":{"kind":"list like","value":["self.src == self.dst: # The src=dst mode should be used if and only","else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs =","hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s","snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be","try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost","import logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def","= set() for glob in self.globs: for path in basepath.glob(glob): if not path.is_file()","= 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path)","src_files, dst_files): existing = 0 disappeared = 0 changes = 0 for i,","dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if","Astacus controls the lifecycle of the # files within). In that case, there","changed. The output to outside is just root object's hash, as well as","to within the file list contained in root object. Note that any call","that any call to public API MUST be made with snapshotter.lock held. This","typing import Optional import base64 import hashlib import logging import os import threading","if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile:","# error, we rather handle it here than leave # exceptions not handled.","threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in self.globs: for","ignoring the files which have changed. The output to outside is just root","enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new","changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but","self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return","continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig,","relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same =","of other hashes which correspond to files referred to within the file list","relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes =","not need to be built-in to Snapshotter, but having it there enables asserting","= path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath)","progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present; dst is","old running' is intentional feature but new operation should eventually replace the old).","need to be built-in to Snapshotter, but having it there enables asserting its","0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src","h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and their hashes.","incremential snapshot and-or ignoring the files which have changed. The output to outside","parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob","self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d.","old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same -","- disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress]","directory: %r\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files,","or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name ==","for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return","def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes","changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert","self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() #","class Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and their hashes. The","Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst","before stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest","1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except FileNotFoundError:","== snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s","Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and their hashes. The hash","threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h =","assert globs # model has empty; either plugin or configuration must supply them","be started with old running' is intentional feature but new operation should eventually","increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1 return changes def","dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1 return","%r\", i - disappeared, relative_path) changes += 1 return changes def snapshot(self, *,","not exactly flawless (the 'new operation can be started with old running' is","in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def","The hash on disk MAY change, which may require subsequent incremential snapshot and-or","len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success()","case, there is little point in # making extra symlinks and we can","sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile)","In that case, there is little point in # making extra symlinks and","def _cb(snapshotfile): # src may or may not be present; dst is present","object's hash, as well as list of other hashes which correspond to files","import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path","leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already","None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst:","return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles)","logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost, self.src / relative_path) continue","import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h","Optional[Progress] = None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs,","= Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The","f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track","is shared across operations, possibly used from multiple threads, and the single-operation-only mode","it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64","increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1 return changes def","self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d.","= 0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try:","which have changed. The output to outside is just root object's hash, as","on disk, and their hashes. The hash on disk MAY change, which may","same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [","files referred to within the file list contained in root object. Note that","map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel)","from multiple threads, and the single-operation-only mode of operation is not exactly flawless","dst_files): existing = 0 disappeared = 0 changes = 0 for i, relative_path","file: %r\", i - disappeared, relative_path) changes += 1 return changes def snapshot(self,","return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started","The output to outside is just root object's hash, as well as list","increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost, self.src / relative_path)","def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes =","== snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src","fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success() return changes","def __init__(self, *, src, dst, globs, parallel): assert globs # model has empty;","_cb(snapshotfile): # src may or may not be present; dst is present as","there is little point in # making extra symlinks and we can just","i - disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress:","controls the lifecycle of the # files within). In that case, there is","MUST be made with snapshotter.lock held. This is because Snapshotter is process-wide utility","hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not","used if and only if it is # known that files will not","FileExistsError: # This happens only if snapshot is started twice at # same","%s disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d.","progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing","enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile)","0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path","Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {}","asserting its state during public API calls. \"\"\" def __init__(self, *, src, dst,","self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles","create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files =","as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f)","snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in","return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0","to public API MUST be made with snapshotter.lock held. This is because Snapshotter","old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same +=","parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files","snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes +=","files will not disappear between snapshot and # upload steps (e.g. Astacus controls","+= 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\", same,","*, src_files, dst_files): existing = 0 disappeared = 0 changes = 0 for","if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del","%s disappeared before stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest","magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress","snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if","os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started","relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else:","that files will not disappear between snapshot and # upload steps (e.g. Astacus","import Optional import base64 import hashlib import logging import os import threading logger","if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes","# probably really worth it and due to ignored files it # actually","p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path,","intentional feature but new operation should eventually replace the old). The lock itself","assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0","changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files)","_snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns,","present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f:","continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break","pathlib import Path from typing import Optional import base64 import hashlib import logging","+= self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success()","if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1 return changes","snapshot and # upload steps (e.g. Astacus controls the lifecycle of the #","logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self,","snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost -","= 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir","here than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d.","Path): files = self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs),","_result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb,","glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue","def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb,","list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present; dst","is not exactly flawless (the 'new operation can be started with old running'","plugin or configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs","use the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else:","basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in","= src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost","already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): #","subsequent incremential snapshot and-or ignoring the files which have changed. The output to","from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import Optional","return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path","data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter","relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing =","snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest =","the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3)","not disappear between snapshot and # upload steps (e.g. Astacus controls the lifecycle","assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf","changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files)","as well as list of other hashes which correspond to files referred to","if not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of","- %r in %s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def","follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started twice at","src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes =","os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000):","must supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile","= None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files","_snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes = 0","files which have changed. The output to outside is just root object's hash,","on disk MAY change, which may require subsequent incremential snapshot and-or ignoring the","self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths):","def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items()","having it there enables asserting its state during public API calls. \"\"\" def","handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path)","list contained in root object. Note that any call to public API MUST","extra symlinks and we can just use the src # directory contents as-is.","only if it is # known that files will not disappear between snapshot","it is # known that files will not disappear between snapshot and #","to files referred to within the file list contained in root object. Note","directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files,","\"\"\"Snapshotter keeps track of files on disk, and their hashes. The hash on","relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if","self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st","{p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile","snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile","as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) #","files within). In that case, there is little point in # making extra","snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is","referred to within the file list contained in root object. Note that any","progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress = Progress()","0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True,","relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i):","if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost, self.src /","files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in","%r\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files):","dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if","stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64","# known that files will not disappear between snapshot and # upload steps","probably really worth it and due to ignored files it # actually might","exactly flawless (the 'new operation can be started with old running' is intentional","time. While it is technically speaking upstream # error, we rather handle it","src, dst, globs, parallel): assert globs # model has empty; either plugin or","assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src)","except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\",","+= 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue","same time. While it is technically speaking upstream # error, we rather handle","dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create","/ relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes","snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles,","is because Snapshotter is process-wide utility that is shared across operations, possibly used","as list of other hashes which correspond to files referred to within the","new operation should eventually replace the old). The lock itself might not need","call to public API MUST be made with snapshotter.lock held. This is because","also remove extra directories, but it is not # probably really worth it","started with old running' is intentional feature but new operation should eventually replace","disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes += 1 return","list of other hashes which correspond to files referred to within the file","self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path):","of operation is not exactly flawless (the 'new operation can be started with","is intentional feature but new operation should eventually replace the old). The lock","src may or may not be present; dst is present as it is","True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter:","self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path)","base64 import hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash","break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs","snapshot is started twice at # same time. While it is technically speaking","and only if it is # known that files will not disappear between","# This happens only if snapshot is started twice at # same time.","disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new","+= 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared","if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st =","is technically speaking upstream # error, we rather handle it here than leave","We COULD also remove extra directories, but it is not # probably really","+= 1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked()","Note that any call to public API MUST be made with snapshotter.lock held.","sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values()))","is just root object's hash, as well as list of other hashes which","is # known that files will not disappear between snapshot and # upload","return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def","progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src","The src=dst mode should be used if and only if it is #","def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile)","else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return","_get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile","progress.add_success() # We COULD also remove extra directories, but it is not #","+= 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except","change, which may require subsequent incremential snapshot and-or ignoring the files which have","/ relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError:","logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared):","we can just use the src # directory contents as-is. dst_dirs, dst_files =","globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock()","self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() #","not even work. # Then, create/update corresponding snapshotfile objects (old # ones were","the single-operation-only mode of operation is not exactly flawless (the 'new operation can","were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile):","be present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as","held. This is because Snapshotter is process-wide utility that is shared across operations,","upload steps (e.g. Astacus controls the lifecycle of the # files within). In","but having it there enables asserting its state during public API calls. \"\"\"","process-wide utility that is shared across operations, possibly used from multiple threads, and","# model has empty; either plugin or configuration must supply them self.src =","if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path):","handle it here than leave # exceptions not handled. existing += 1 if","due to ignored files it # actually might not even work. # Then,","= self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost):","technically speaking upstream # error, we rather handle it here than leave #","it there enables asserting its state during public API calls. \"\"\" def __init__(self,","track of files on disk, and their hashes. The hash on disk MAY","relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot","configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs","+= 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for","snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self,","dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst","exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1 return","except FileExistsError: # This happens only if snapshot is started twice at #","src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared,","Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes","it and due to ignored files it # actually might not even work.","changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst /","which may require subsequent incremential snapshot and-or ignoring the files which have changed.","h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and","= self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False)","dst, globs, parallel): assert globs # model has empty; either plugin or configuration","little point in # making extra symlinks and we can just use the","missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra","public API MUST be made with snapshotter.lock held. This is because Snapshotter is","same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)","relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1","return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i,","removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src","for glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink():","= _hash() while True: data = f.read(read_buffer) if not data: break h.update(data) return","if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if","mode of operation is not exactly flawless (the 'new operation can be started","dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add","snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\",","work. # Then, create/update corresponding snapshotfile objects (old # ones were already removed)","to be built-in to Snapshotter, but having it there enables asserting its state","files = self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files","= {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self,","or may not be present; dst is present as it is in snapshot","FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared,","dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it is not","files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path]","progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: #","API MUST be made with snapshotter.lock held. This is because Snapshotter is process-wide","path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return","disappear between snapshot and # upload steps (e.g. Astacus controls the lifecycle of","parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def","f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return","self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel","empty; either plugin or configuration must supply them self.src = Path(src) self.dst =","] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs):","relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size)","dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1","0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile","old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest,","but it is not # probably really worth it and due to ignored","state during public API calls. \"\"\" def __init__(self, *, src, dst, globs, parallel):","yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf","snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1","changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0","error, we rather handle it here than leave # exceptions not handled. existing","result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent","snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress","logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1","will not disappear between snapshot and # upload steps (e.g. Astacus controls the","in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath","= {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile):","Ltd See LICENSE for details \"\"\" from astacus.common import magic, utils from astacus.common.ipc","path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR:","/ relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same","hash, as well as list of other hashes which correspond to files referred","Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel","This is because Snapshotter is process-wide utility that is shared across operations, possibly","= old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same","enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst / relative_path try:","/ relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if","(e.g. Astacus controls the lifecycle of the # files within). In that case,","files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories,","relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: #","directories, but it is not # probably really worth it and due to","hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes +=","return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and their","files on disk, and their hashes. The hash on disk MAY change, which","of the # files within). In that case, there is little point in","Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes","= self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s","self.dst: # The src=dst mode should be used if and only if it","just root object's hash, as well as list of other hashes which correspond","= {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files","size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked()","should be used if and only if it is # known that files","outside is just root object's hash, as well as list of other hashes","missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes +=","root object's hash, as well as list of other hashes which correspond to","src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs,","logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True:","or configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs =","relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile ==","API calls. \"\"\" def __init__(self, *, src, dst, globs, parallel): assert globs #","= self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files def","if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile","as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE:","either plugin or configuration must supply them self.src = Path(src) self.dst = Path(dst)","object. Note that any call to public API MUST be made with snapshotter.lock","self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra","self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not","astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import Optional import","operations, possibly used from multiple threads, and the single-operation-only mode of operation is","replace the old). The lock itself might not need to be built-in to","2020 Aiven Ltd See LICENSE for details \"\"\" from astacus.common import magic, utils","might not need to be built-in to Snapshotter, but having it there enables","disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i -","logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f,","sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs,","relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath:","changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src /","relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] = None):","= self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success()","from typing import Optional import base64 import hashlib import logging import os import","shared across operations, possibly used from multiple threads, and the single-operation-only mode of","eventually replace the old). The lock itself might not need to be built-in","snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path]","src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost =","extra file: %r\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *,","within). In that case, there is little point in # making extra symlinks","[]).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest:","changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1","feature but new operation should eventually replace the old). The lock itself might","0 disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1):","self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except","old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same):","in %s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert","if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *,","src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path =","has empty; either plugin or configuration must supply them self.src = Path(src) self.dst","during public API calls. \"\"\" def __init__(self, *, src, dst, globs, parallel): assert","self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only","continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking,","between snapshot and # upload steps (e.g. Astacus controls the lifecycle of the","# directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files","= Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles =","self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert","basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in files} return","ignored files it # actually might not even work. # Then, create/update corresponding","_add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile","relative_paths): same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile =","/ relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile","def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None:","= self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove","extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes +=","self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self,","dirs = {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile:","# The src=dst mode should be used if and only if it is","hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer)","its state during public API calls. \"\"\" def __init__(self, *, src, dst, globs,","changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in","within the file list contained in root object. Note that any call to","# We COULD also remove extra directories, but it is not # probably","SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert","the file list contained in root object. Note that any call to public","set() for glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or","_remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def","dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() #","= self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i):","not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if","self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path]","1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\", same, old_snapshotfile,","in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile:","self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it is","self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for","remove extra directories, but it is not # probably really worth it and","files it # actually might not even work. # Then, create/update corresponding snapshotfile","src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before","i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst","may not be present; dst is present as it is in snapshot with","old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile):","is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src ==","is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 =","because Snapshotter is process-wide utility that is shared across operations, possibly used from","def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files),","is started twice at # same time. While it is technically speaking upstream","but new operation should eventually replace the old). The lock itself might not","else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs,","True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with","snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d.","%s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked()","= self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens","import Path from typing import Optional import base64 import hashlib import logging import","point in # making extra symlinks and we can just use the src","is not # probably really worth it and due to ignored files it","# Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files","increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i -","the files which have changed. The output to outside is just root object's","for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True)","it is technically speaking upstream # error, we rather handle it here than","*, src, dst, globs, parallel): assert globs # model has empty; either plugin","globs, parallel): assert globs # model has empty; either plugin or configuration must","same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig,","Aiven Ltd See LICENSE for details \"\"\" from astacus.common import magic, utils from","magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in,","{} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath:","(c) 2020 Aiven Ltd See LICENSE for details \"\"\" from astacus.common import magic,","started twice at # same time. While it is technically speaking upstream #","old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size)","Then, create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files","with old running' is intentional feature but new operation should eventually replace the","changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst /","hashes which correspond to files referred to within the file list contained in","SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path):","__init__(self, *, src, dst, globs, parallel): assert globs # model has empty; either","_snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1):","enables asserting its state during public API calls. \"\"\" def __init__(self, *, src,","public API calls. \"\"\" def __init__(self, *, src, dst, globs, parallel): assert globs","file: %r\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files,","self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock","and # upload steps (e.g. Astacus controls the lifecycle of the # files","can just use the src # directory contents as-is. dst_dirs, dst_files = src_dirs,","existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d.","None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files =","0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except","supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile =","base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success()","*, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress =","that case, there is little point in # making extra symlinks and we","operation should eventually replace the old). The lock itself might not need to","dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success()","made with snapshotter.lock held. This is because Snapshotter is process-wide utility that is","can be started with old running' is intentional feature but new operation should","map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) #","1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if","# Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also","= Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel =","= logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while","utility that is shared across operations, possibly used from multiple threads, and the","the old). The lock itself might not need to be built-in to Snapshotter,","for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path =","lock itself might not need to be built-in to Snapshotter, but having it","already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared):","contained in root object. Note that any call to public API MUST be","old). The lock itself might not need to be built-in to Snapshotter, but","in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None)","dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst","def _list_files(self, basepath: Path): result_files = set() for glob in self.globs: for path","if self.src == self.dst: # The src=dst mode should be used if and","if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r","COULD also remove extra directories, but it is not # probably really worth","assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path","be made with snapshotter.lock held. This is because Snapshotter is process-wide utility that","if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same","progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD","Path from typing import Optional import base64 import hashlib import logging import os","i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if","This happens only if snapshot is started twice at # same time. While","known that files will not disappear between snapshot and # upload steps (e.g.","lost - %s disappeared before stat, ignoring\", lost, self.src / relative_path) continue if","file list contained in root object. Note that any call to public API","existing = 0 disappeared = 0 changes = 0 for i, relative_path in","dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may","other hashes which correspond to files referred to within the file list contained","1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory:","continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path)","src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes","any call to public API MUST be made with snapshotter.lock held. This is","to ignored files it # actually might not even work. # Then, create/update","# actually might not even work. # Then, create/update corresponding snapshotfile objects (old","logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes += 1 return changes","magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath)","self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1)","Optional import base64 import hashlib import logging import os import threading logger =","snapshotter.lock held. This is because Snapshotter is process-wide utility that is shared across","While it is technically speaking upstream # error, we rather handle it here","happens only if snapshot is started twice at # same time. While it","read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not data: break","# Then, create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs,","might not even work. # Then, create/update corresponding snapshotfile objects (old # ones","in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost +=","<= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*,","relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes +=","utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from","import base64 import hashlib import logging import os import threading logger = logging.getLogger(__name__)","present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <=","SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest:","= hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data =","if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1 return changes","logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash()","path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for","file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in","increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes +=","the lifecycle of the # files within). In that case, there is little","= hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes","for details \"\"\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash,","self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path =","astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import","logger.debug(\"#%d. same - %r in %s is same\", same, old_snapshotfile, relative_path) continue yield","result_files = set() for glob in self.globs: for path in basepath.glob(glob): if not","snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat()","calls. \"\"\" def __init__(self, *, src, dst, globs, parallel): assert globs # model","_snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1):","snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else:","may or may not be present; dst is present as it is in","operation can be started with old running' is intentional feature but new operation","self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ]","in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self,","Snapshotter, but having it there enables asserting its state during public API calls.","if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i","== self.dst: # The src=dst mode should be used if and only if","del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path","snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out):","before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file:","disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path)","The lock itself might not need to be built-in to Snapshotter, but having","from pathlib import Path from typing import Optional import base64 import hashlib import","not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing,","dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing","SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir","# ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles))","if it is # known that files will not disappear between snapshot and","be used if and only if it is # known that files will","if and only if it is # known that files will not disappear","if snapshot is started twice at # same time. While it is technically","Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove","threads, and the single-operation-only mode of operation is not exactly flawless (the 'new","while True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class","globs # model has empty; either plugin or configuration must supply them self.src","sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p","# same time. While it is technically speaking upstream # error, we rather","new file: %r\", i - disappeared, relative_path) changes += 1 return changes def","dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs,","we rather handle it here than leave # exceptions not handled. existing +=","\"\"\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from","lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\",","for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath)","= 0 disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files),","objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles =","if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes","*, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path","progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially","MAY change, which may require subsequent incremential snapshot and-or ignoring the files which","1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost, self.src","corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)","_hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data","across operations, possibly used from multiple threads, and the single-operation-only mode of operation","ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 =","== magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files =","disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] =","not # probably really worth it and due to ignored files it #","+= len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra","+= 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost,","def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in","linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\",","and the single-operation-only mode of operation is not exactly flawless (the 'new operation","1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink()","# Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files","return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile:","%r\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files):","and their hashes. The hash on disk MAY change, which may require subsequent","self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We","in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent","FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat,","/ relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra","ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s","used from multiple threads, and the single-operation-only mode of operation is not exactly","progress.start(1) if self.src == self.dst: # The src=dst mode should be used if","dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or","be built-in to Snapshotter, but having it there enables asserting its state during","existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared","just use the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files","[ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self):","snapshotfile objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles","See LICENSE for details \"\"\" from astacus.common import magic, utils from astacus.common.ipc import","possibly used from multiple threads, and the single-operation-only mode of operation is not","= old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if","%r in %s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self):","ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def","= globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock =","def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if","SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import","it is not # probably really worth it and due to ignored files","upstream # error, we rather handle it here than leave # exceptions not","%s already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if","basepath: Path): result_files = set() for glob in self.globs: for path in basepath.glob(glob):","Path): result_files = set() for glob in self.globs: for path in basepath.glob(glob): if","= self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i,","changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes","return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress","progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs)","ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i","return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0","import increase_worth_reporting, Progress from pathlib import Path from typing import Optional import base64","rather handle it here than leave # exceptions not handled. existing += 1","self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self,","is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size","root object. Note that any call to public API MUST be made with","None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self,","from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib","self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath =","keeps track of files on disk, and their hashes. The hash on disk","hash on disk MAY change, which may require subsequent incremential snapshot and-or ignoring","def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in relative_paths:","only if snapshot is started twice at # same time. While it is","lifecycle of the # files within). In that case, there is little point","i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)","dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\",","self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def","existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue","Progress from pathlib import Path from typing import Optional import base64 import hashlib","SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from","# upload steps (e.g. Astacus controls the lifecycle of the # files within).","their hashes. The hash on disk MAY change, which may require subsequent incremential","new directory: %r\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *,","def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs),","(old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files))","# making extra symlinks and we can just use the src # directory","there enables asserting its state during public API calls. \"\"\" def __init__(self, *,","Snapshotter is process-wide utility that is shared across operations, possibly used from multiple","with snapshotter.lock held. This is because Snapshotter is process-wide utility that is shared","changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is","twice at # same time. While it is technically speaking upstream # error,","self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should be used","exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\",","speaking upstream # error, we rather handle it here than leave # exceptions","at # same time. While it is technically speaking upstream # error, we","old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if","= 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path","import hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash =","# src may or may not be present; dst is present as it","running' is intentional feature but new operation should eventually replace the old). The","= parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for","changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0","for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile =","steps (e.g. Astacus controls the lifecycle of the # files within). In that","self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1","snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def","snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src /","operation is not exactly flawless (the 'new operation can be started with old","SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for","# exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed,","path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name","'new operation can be started with old running' is intentional feature but new","single-operation-only mode of operation is not exactly flawless (the 'new operation can be","= 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path","from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress","relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file:","symlinks and we can just use the src # directory contents as-is. dst_dirs,","\"\"\" def __init__(self, *, src, dst, globs, parallel): assert globs # model has","= self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile)","= self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may","directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files =","= self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self,","0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile","files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if","= 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path =","_hash() while True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest()","def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path,","1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue if","may require subsequent incremential snapshot and-or ignoring the files which have changed. The","require subsequent incremential snapshot and-or ignoring the files which have changed. The output","them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {}","1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i,","parallel): assert globs # model has empty; either plugin or configuration must supply","that is shared across operations, possibly used from multiple threads, and the single-operation-only","for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile =","src_path = self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path,","- disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes += 1","details \"\"\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState","with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest","self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared","lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile =","in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d.","try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is","*, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path","correspond to files referred to within the file list contained in root object.","src=dst mode should be used if and only if it is # known","# files within). In that case, there is little point in # making","built-in to Snapshotter, but having it there enables asserting its state during public","and-or ignoring the files which have changed. The output to outside is just","it here than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing):","disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path","= threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in self.globs:","self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set()","except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before","*, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not data:","disk MAY change, which may require subsequent incremential snapshot and-or ignoring the files","= f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps","increase_worth_reporting, Progress from pathlib import Path from typing import Optional import base64 import","for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files)","self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def","should eventually replace the old). The lock itself might not need to be","of files on disk, and their hashes. The hash on disk MAY change,","is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return","to Snapshotter, but having it there enables asserting its state during public API","if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\", same, old_snapshotfile, relative_path)","dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started twice","have changed. The output to outside is just root object's hash, as well","than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s","files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files,","LICENSE for details \"\"\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile,","well as list of other hashes which correspond to files referred to within","worth it and due to ignored files it # actually might not even","in root object. Note that any call to public API MUST be made","\"\"\" Copyright (c) 2020 Aiven Ltd See LICENSE for details \"\"\" from astacus.common","astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import","_list_files(self, basepath: Path): result_files = set() for glob in self.globs: for path in","continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile:","lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64","{} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files =","same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\",","model has empty; either plugin or configuration must supply them self.src = Path(src)","actually might not even work. # Then, create/update corresponding snapshotfile objects (old #","utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success() return","def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] =","_list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in files}","i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes","old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in","making extra symlinks and we can just use the src # directory contents","= src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories","data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on","mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path","dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We","in # making extra symlinks and we can just use the src #","multiple threads, and the single-operation-only mode of operation is not exactly flawless (the","snapshot and-or ignoring the files which have changed. The output to outside is","increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared +=","relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost","mode should be used if and only if it is # known that","hashes. The hash on disk MAY change, which may require subsequent incremential snapshot","contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)","+= self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it","output to outside is just root object's hash, as well as list of","get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if","st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0","if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents:","(the 'new operation can be started with old running' is intentional feature but","- %s disappeared before stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile:","= snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] ==","and we can just use the src # directory contents as-is. dst_dirs, dst_files","import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting,","import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *,","self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if","disk, and their hashes. The hash on disk MAY change, which may require","the # files within). In that case, there is little point in #","= base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out)","disappeared before stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest =","logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self,","h = _hash() while True: data = f.read(read_buffer) if not data: break h.update(data)","really worth it and due to ignored files it # actually might not","src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path =","Copyright (c) 2020 Aiven Ltd See LICENSE for details \"\"\" from astacus.common import","and due to ignored files it # actually might not even work. #","it # actually might not even work. # Then, create/update corresponding snapshotfile objects","= list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present;","1): src_path = self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path,","same - %r in %s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile","self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in","if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared","to outside is just root object's hash, as well as list of other","src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode","for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError:","flawless (the 'new operation can be started with old running' is intentional feature","SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing","itself might not need to be built-in to Snapshotter, but having it there","src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def","self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if","increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\", same, old_snapshotfile, relative_path) continue","extra directories, but it is not # probably really worth it and due","dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This","not be present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst)","src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should","return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for","self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir)","i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing","break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on disk,","even work. # Then, create/update corresponding snapshotfile objects (old # ones were already","= self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should be","not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files","which correspond to files referred to within the file list contained in root","1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared =","in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode()","snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True","= self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i,","relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst /","is little point in # making extra symlinks and we can just use","get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes =","relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for","in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst / relative_path","is process-wide utility that is shared across operations, possibly used from multiple threads,"],"string":"[\n \"self.src == self.dst: # The src=dst mode should be used if and only\",\n \"else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs =\",\n \"hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s\",\n \"snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be\",\n \"try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost\",\n \"import logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def\",\n \"= set() for glob in self.globs: for path in basepath.glob(glob): if not path.is_file()\",\n \"= 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path)\",\n \"src_files, dst_files): existing = 0 disappeared = 0 changes = 0 for i,\",\n \"dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if\",\n \"Astacus controls the lifecycle of the # files within). In that case, there\",\n \"changed. The output to outside is just root object's hash, as well as\",\n \"to within the file list contained in root object. Note that any call\",\n \"that any call to public API MUST be made with snapshotter.lock held. This\",\n \"typing import Optional import base64 import hashlib import logging import os import threading\",\n \"if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile:\",\n \"# error, we rather handle it here than leave # exceptions not handled.\",\n \"threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in self.globs: for\",\n \"ignoring the files which have changed. The output to outside is just root\",\n \"enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new\",\n \"changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but\",\n \"self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return\",\n \"continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig,\",\n \"relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same =\",\n \"of other hashes which correspond to files referred to within the file list\",\n \"relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes =\",\n \"not need to be built-in to Snapshotter, but having it there enables asserting\",\n \"= path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath)\",\n \"progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present; dst is\",\n \"old running' is intentional feature but new operation should eventually replace the old).\",\n \"need to be built-in to Snapshotter, but having it there enables asserting its\",\n \"0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src\",\n \"h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and their hashes.\",\n \"incremential snapshot and-or ignoring the files which have changed. The output to outside\",\n \"parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob\",\n \"self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d.\",\n \"old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same -\",\n \"- disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress]\",\n \"directory: %r\\\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files,\",\n \"or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name ==\",\n \"for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return\",\n \"def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes\",\n \"changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert\",\n \"self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() #\",\n \"class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and their hashes. The\",\n \"Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst\",\n \"before stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest\",\n \"1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError:\",\n \"== snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s\",\n \"Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and their hashes. The hash\",\n \"threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h =\",\n \"assert globs # model has empty; either plugin or configuration must supply them\",\n \"be started with old running' is intentional feature but new operation should eventually\",\n \"increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1 return changes def\",\n \"dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1 return\",\n \"%r\\\", i - disappeared, relative_path) changes += 1 return changes def snapshot(self, *,\",\n \"not exactly flawless (the 'new operation can be started with old running' is\",\n \"in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def\",\n \"The hash on disk MAY change, which may require subsequent incremential snapshot and-or\",\n \"len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success()\",\n \"case, there is little point in # making extra symlinks and we can\",\n \"sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile)\",\n \"In that case, there is little point in # making extra symlinks and\",\n \"def _cb(snapshotfile): # src may or may not be present; dst is present\",\n \"object's hash, as well as list of other hashes which correspond to files\",\n \"import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path\",\n \"leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already\",\n \"None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst:\",\n \"return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles)\",\n \"logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue\",\n \"import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h\",\n \"Optional[Progress] = None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs,\",\n \"= Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The\",\n \"f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track\",\n \"is shared across operations, possibly used from multiple threads, and the single-operation-only mode\",\n \"it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64\",\n \"increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1 return changes def\",\n \"self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d.\",\n \"= 0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try:\",\n \"which have changed. The output to outside is just root object's hash, as\",\n \"on disk, and their hashes. The hash on disk MAY change, which may\",\n \"same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [\",\n \"files referred to within the file list contained in root object. Note that\",\n \"map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel)\",\n \"from multiple threads, and the single-operation-only mode of operation is not exactly flawless\",\n \"dst_files): existing = 0 disappeared = 0 changes = 0 for i, relative_path\",\n \"file: %r\\\", i - disappeared, relative_path) changes += 1 return changes def snapshot(self,\",\n \"return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started\",\n \"The output to outside is just root object's hash, as well as list\",\n \"increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost, self.src / relative_path)\",\n \"def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes =\",\n \"== snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src\",\n \"fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success() return changes\",\n \"def __init__(self, *, src, dst, globs, parallel): assert globs # model has empty;\",\n \"_cb(snapshotfile): # src may or may not be present; dst is present as\",\n \"there is little point in # making extra symlinks and we can just\",\n \"i - disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress:\",\n \"controls the lifecycle of the # files within). In that case, there is\",\n \"MUST be made with snapshotter.lock held. This is because Snapshotter is process-wide utility\",\n \"hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not\",\n \"used if and only if it is # known that files will not\",\n \"FileExistsError: # This happens only if snapshot is started twice at # same\",\n \"%s disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d.\",\n \"progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing\",\n \"enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile)\",\n \"0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path\",\n \"Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {}\",\n \"asserting its state during public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst,\",\n \"self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles\",\n \"create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files =\",\n \"as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f)\",\n \"snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in\",\n \"return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0\",\n \"to public API MUST be made with snapshotter.lock held. This is because Snapshotter\",\n \"old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same +=\",\n \"parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files\",\n \"snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes +=\",\n \"files will not disappear between snapshot and # upload steps (e.g. Astacus controls\",\n \"+= 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\", same,\",\n \"*, src_files, dst_files): existing = 0 disappeared = 0 changes = 0 for\",\n \"if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del\",\n \"%s disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest\",\n \"magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress\",\n \"snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if\",\n \"os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started\",\n \"relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else:\",\n \"that files will not disappear between snapshot and # upload steps (e.g. Astacus\",\n \"import Optional import base64 import hashlib import logging import os import threading logger\",\n \"if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes\",\n \"# probably really worth it and due to ignored files it # actually\",\n \"p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path,\",\n \"intentional feature but new operation should eventually replace the old). The lock itself\",\n \"assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0\",\n \"changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files)\",\n \"_snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns,\",\n \"present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f:\",\n \"continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break\",\n \"pathlib import Path from typing import Optional import base64 import hashlib import logging\",\n \"+= self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success()\",\n \"if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1 return changes\",\n \"snapshot and # upload steps (e.g. Astacus controls the lifecycle of the #\",\n \"logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self,\",\n \"snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost -\",\n \"= 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir\",\n \"here than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d.\",\n \"Path): files = self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs),\",\n \"_result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb,\",\n \"glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue\",\n \"def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb,\",\n \"list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present; dst\",\n \"is not exactly flawless (the 'new operation can be started with old running'\",\n \"plugin or configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs\",\n \"use the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else:\",\n \"basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in\",\n \"= src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost\",\n \"already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): #\",\n \"subsequent incremential snapshot and-or ignoring the files which have changed. The output to\",\n \"from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import Optional\",\n \"return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path\",\n \"data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter\",\n \"relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing =\",\n \"snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest =\",\n \"the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3)\",\n \"not disappear between snapshot and # upload steps (e.g. Astacus controls the lifecycle\",\n \"assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf\",\n \"changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files)\",\n \"as well as list of other hashes which correspond to files referred to\",\n \"if not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of\",\n \"- %r in %s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def\",\n \"follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started twice at\",\n \"src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes =\",\n \"os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000):\",\n \"must supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile\",\n \"= None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files\",\n \"_snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes = 0\",\n \"files which have changed. The output to outside is just root object's hash,\",\n \"on disk MAY change, which may require subsequent incremential snapshot and-or ignoring the\",\n \"self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths):\",\n \"def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items()\",\n \"having it there enables asserting its state during public API calls. \\\"\\\"\\\" def\",\n \"handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path)\",\n \"list contained in root object. Note that any call to public API MUST\",\n \"extra symlinks and we can just use the src # directory contents as-is.\",\n \"only if it is # known that files will not disappear between snapshot\",\n \"it is # known that files will not disappear between snapshot and #\",\n \"to files referred to within the file list contained in root object. Note\",\n \"directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files,\",\n \"\\\"\\\"\\\"Snapshotter keeps track of files on disk, and their hashes. The hash on\",\n \"relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st\",\n \"{p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile\",\n \"snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile\",\n \"as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) #\",\n \"files within). In that case, there is little point in # making extra\",\n \"snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is\",\n \"referred to within the file list contained in root object. Note that any\",\n \"progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress = Progress()\",\n \"0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True,\",\n \"relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i):\",\n \"if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost, self.src /\",\n \"files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in\",\n \"%r\\\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files):\",\n \"dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if\",\n \"stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64\",\n \"# known that files will not disappear between snapshot and # upload steps\",\n \"probably really worth it and due to ignored files it # actually might\",\n \"exactly flawless (the 'new operation can be started with old running' is intentional\",\n \"time. While it is technically speaking upstream # error, we rather handle it\",\n \"src, dst, globs, parallel): assert globs # model has empty; either plugin or\",\n \"assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src)\",\n \"except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\",\",\n \"+= 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue\",\n \"same time. While it is technically speaking upstream # error, we rather handle\",\n \"dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create\",\n \"/ relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes\",\n \"snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles,\",\n \"is because Snapshotter is process-wide utility that is shared across operations, possibly used\",\n \"as list of other hashes which correspond to files referred to within the\",\n \"new operation should eventually replace the old). The lock itself might not need\",\n \"call to public API MUST be made with snapshotter.lock held. This is because\",\n \"also remove extra directories, but it is not # probably really worth it\",\n \"started with old running' is intentional feature but new operation should eventually replace\",\n \"disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes += 1 return\",\n \"list of other hashes which correspond to files referred to within the file\",\n \"self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path):\",\n \"of operation is not exactly flawless (the 'new operation can be started with\",\n \"is intentional feature but new operation should eventually replace the old). The lock\",\n \"src may or may not be present; dst is present as it is\",\n \"True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter:\",\n \"self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path)\",\n \"base64 import hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash\",\n \"break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs\",\n \"snapshot is started twice at # same time. While it is technically speaking\",\n \"and only if it is # known that files will not disappear between\",\n \"# This happens only if snapshot is started twice at # same time.\",\n \"disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new\",\n \"+= 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared\",\n \"if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st =\",\n \"is technically speaking upstream # error, we rather handle it here than leave\",\n \"We COULD also remove extra directories, but it is not # probably really\",\n \"+= 1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked()\",\n \"Note that any call to public API MUST be made with snapshotter.lock held.\",\n \"sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values()))\",\n \"is just root object's hash, as well as list of other hashes which\",\n \"is # known that files will not disappear between snapshot and # upload\",\n \"return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def\",\n \"progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src\",\n \"The src=dst mode should be used if and only if it is #\",\n \"def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile)\",\n \"else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return\",\n \"_get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile\",\n \"progress.add_success() # We COULD also remove extra directories, but it is not #\",\n \"+= 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except\",\n \"change, which may require subsequent incremential snapshot and-or ignoring the files which have\",\n \"/ relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError:\",\n \"logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared):\",\n \"we can just use the src # directory contents as-is. dst_dirs, dst_files =\",\n \"globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock()\",\n \"self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() #\",\n \"not even work. # Then, create/update corresponding snapshotfile objects (old # ones were\",\n \"the single-operation-only mode of operation is not exactly flawless (the 'new operation can\",\n \"were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile):\",\n \"be present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as\",\n \"held. This is because Snapshotter is process-wide utility that is shared across operations,\",\n \"upload steps (e.g. Astacus controls the lifecycle of the # files within). In\",\n \"but having it there enables asserting its state during public API calls. \\\"\\\"\\\"\",\n \"process-wide utility that is shared across operations, possibly used from multiple threads, and\",\n \"# model has empty; either plugin or configuration must supply them self.src =\",\n \"if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path):\",\n \"handle it here than leave # exceptions not handled. existing += 1 if\",\n \"due to ignored files it # actually might not even work. # Then,\",\n \"= self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost):\",\n \"technically speaking upstream # error, we rather handle it here than leave #\",\n \"it there enables asserting its state during public API calls. \\\"\\\"\\\" def __init__(self,\",\n \"track of files on disk, and their hashes. The hash on disk MAY\",\n \"relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot\",\n \"configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs\",\n \"+= 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for\",\n \"snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self,\",\n \"dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst\",\n \"exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1 return\",\n \"except FileExistsError: # This happens only if snapshot is started twice at #\",\n \"src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared,\",\n \"Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes\",\n \"it and due to ignored files it # actually might not even work.\",\n \"changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst /\",\n \"which may require subsequent incremential snapshot and-or ignoring the files which have changed.\",\n \"h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and\",\n \"= self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False)\",\n \"dst, globs, parallel): assert globs # model has empty; either plugin or configuration\",\n \"little point in # making extra symlinks and we can just use the\",\n \"missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra\",\n \"public API MUST be made with snapshotter.lock held. This is because Snapshotter is\",\n \"same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)\",\n \"relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1\",\n \"return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i,\",\n \"removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src\",\n \"for glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink():\",\n \"= _hash() while True: data = f.read(read_buffer) if not data: break h.update(data) return\",\n \"if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if\",\n \"mode of operation is not exactly flawless (the 'new operation can be started\",\n \"dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add\",\n \"snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\",\",\n \"work. # Then, create/update corresponding snapshotfile objects (old # ones were already removed)\",\n \"to be built-in to Snapshotter, but having it there enables asserting its state\",\n \"files = self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files\",\n \"= {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self,\",\n \"or may not be present; dst is present as it is in snapshot\",\n \"FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared,\",\n \"dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it is not\",\n \"files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: #\",\n \"API MUST be made with snapshotter.lock held. This is because Snapshotter is process-wide\",\n \"path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return\",\n \"disappear between snapshot and # upload steps (e.g. Astacus controls the lifecycle of\",\n \"parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def\",\n \"f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return\",\n \"self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel\",\n \"empty; either plugin or configuration must supply them self.src = Path(src) self.dst =\",\n \"] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs):\",\n \"relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size)\",\n \"dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1\",\n \"0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile\",\n \"old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest,\",\n \"but it is not # probably really worth it and due to ignored\",\n \"state during public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel):\",\n \"yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf\",\n \"snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1\",\n \"changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0\",\n \"error, we rather handle it here than leave # exceptions not handled. existing\",\n \"result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent\",\n \"snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress\",\n \"logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1\",\n \"will not disappear between snapshot and # upload steps (e.g. Astacus controls the\",\n \"in self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath\",\n \"= {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile):\",\n \"Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc\",\n \"path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR:\",\n \"/ relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same\",\n \"hash, as well as list of other hashes which correspond to files referred\",\n \"Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel\",\n \"This is because Snapshotter is process-wide utility that is shared across operations, possibly\",\n \"= old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same\",\n \"enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst / relative_path try:\",\n \"/ relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if\",\n \"(e.g. Astacus controls the lifecycle of the # files within). In that case,\",\n \"files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories,\",\n \"relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: #\",\n \"directories, but it is not # probably really worth it and due to\",\n \"hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes +=\",\n \"return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and their\",\n \"files on disk, and their hashes. The hash on disk MAY change, which\",\n \"of the # files within). In that case, there is little point in\",\n \"Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes\",\n \"= self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s\",\n \"self.dst: # The src=dst mode should be used if and only if it\",\n \"just root object's hash, as well as list of other hashes which correspond\",\n \"= {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files\",\n \"size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked()\",\n \"should be used if and only if it is # known that files\",\n \"outside is just root object's hash, as well as list of other hashes\",\n \"missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes +=\",\n \"root object's hash, as well as list of other hashes which correspond to\",\n \"src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs,\",\n \"logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True:\",\n \"or configuration must supply them self.src = Path(src) self.dst = Path(dst) self.globs =\",\n \"relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile ==\",\n \"API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert globs #\",\n \"= self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files def\",\n \"if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile\",\n \"as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE:\",\n \"either plugin or configuration must supply them self.src = Path(src) self.dst = Path(dst)\",\n \"object. Note that any call to public API MUST be made with snapshotter.lock\",\n \"self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra\",\n \"self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not\",\n \"astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import Optional import\",\n \"operations, possibly used from multiple threads, and the single-operation-only mode of operation is\",\n \"replace the old). The lock itself might not need to be built-in to\",\n \"2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils\",\n \"might not need to be built-in to Snapshotter, but having it there enables\",\n \"disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i -\",\n \"logging import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f,\",\n \"sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs,\",\n \"relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath:\",\n \"changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src /\",\n \"relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] = None):\",\n \"= self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success()\",\n \"from typing import Optional import base64 import hashlib import logging import os import\",\n \"shared across operations, possibly used from multiple threads, and the single-operation-only mode of\",\n \"eventually replace the old). The lock itself might not need to be built-in\",\n \"snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost =\",\n \"extra file: %r\\\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *,\",\n \"within). In that case, there is little point in # making extra symlinks\",\n \"[]).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest:\",\n \"changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1\",\n \"feature but new operation should eventually replace the old). The lock itself might\",\n \"0 disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1):\",\n \"self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except\",\n \"old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same):\",\n \"in %s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert\",\n \"if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *,\",\n \"src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path =\",\n \"has empty; either plugin or configuration must supply them self.src = Path(src) self.dst\",\n \"during public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert\",\n \"self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only\",\n \"continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking,\",\n \"between snapshot and # upload steps (e.g. Astacus controls the lifecycle of the\",\n \"# directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files\",\n \"= Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles =\",\n \"self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert\",\n \"basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in files} return\",\n \"ignored files it # actually might not even work. # Then, create/update corresponding\",\n \"_add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile\",\n \"relative_paths): same = 0 lost = 0 for relative_path in relative_paths: old_snapshotfile =\",\n \"/ relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile\",\n \"def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None:\",\n \"= self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove\",\n \"extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes +=\",\n \"self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self,\",\n \"dirs = {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile:\",\n \"# The src=dst mode should be used if and only if it is\",\n \"hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer)\",\n \"its state during public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs,\",\n \"changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in\",\n \"within the file list contained in root object. Note that any call to\",\n \"# We COULD also remove extra directories, but it is not # probably\",\n \"SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert\",\n \"the file list contained in root object. Note that any call to public\",\n \"set() for glob in self.globs: for path in basepath.glob(glob): if not path.is_file() or\",\n \"_remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def\",\n \"dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() #\",\n \"= self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i):\",\n \"not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\", same, old_snapshotfile,\",\n \"in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile:\",\n \"self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it is\",\n \"self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for\",\n \"remove extra directories, but it is not # probably really worth it and\",\n \"files it # actually might not even work. # Then, create/update corresponding snapshotfile\",\n \"src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before\",\n \"i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst\",\n \"may not be present; dst is present as it is in snapshot with\",\n \"old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile):\",\n \"is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src ==\",\n \"is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 =\",\n \"because Snapshotter is process-wide utility that is shared across operations, possibly used from\",\n \"def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files),\",\n \"is started twice at # same time. While it is technically speaking upstream\",\n \"but new operation should eventually replace the old). The lock itself might not\",\n \"else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs,\",\n \"True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with\",\n \"snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d.\",\n \"%s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked()\",\n \"= self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens\",\n \"import Path from typing import Optional import base64 import hashlib import logging import\",\n \"point in # making extra symlinks and we can just use the src\",\n \"is not # probably really worth it and due to ignored files it\",\n \"# Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files\",\n \"increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i -\",\n \"the files which have changed. The output to outside is just root object's\",\n \"for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True)\",\n \"it is technically speaking upstream # error, we rather handle it here than\",\n \"*, src, dst, globs, parallel): assert globs # model has empty; either plugin\",\n \"globs, parallel): assert globs # model has empty; either plugin or configuration must\",\n \"same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig,\",\n \"Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils from\",\n \"magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in,\",\n \"{} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath:\",\n \"(c) 2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import magic,\",\n \"started twice at # same time. While it is technically speaking upstream #\",\n \"old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size)\",\n \"Then, create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files\",\n \"with old running' is intentional feature but new operation should eventually replace the\",\n \"changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst /\",\n \"hashes which correspond to files referred to within the file list contained in\",\n \"SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path):\",\n \"__init__(self, *, src, dst, globs, parallel): assert globs # model has empty; either\",\n \"_snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1):\",\n \"enables asserting its state during public API calls. \\\"\\\"\\\" def __init__(self, *, src,\",\n \"public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert globs\",\n \"file: %r\\\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files,\",\n \"self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock\",\n \"and # upload steps (e.g. Astacus controls the lifecycle of the # files\",\n \"can just use the src # directory contents as-is. dst_dirs, dst_files = src_dirs,\",\n \"existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d.\",\n \"None): assert self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files =\",\n \"0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except\",\n \"supply them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile =\",\n \"base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success()\",\n \"*, progress: Optional[Progress] = None): assert self.lock.locked() if progress is None: progress =\",\n \"that case, there is little point in # making extra symlinks and we\",\n \"operation should eventually replace the old). The lock itself might not need to\",\n \"dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success()\",\n \"made with snapshotter.lock held. This is because Snapshotter is process-wide utility that is\",\n \"can be started with old running' is intentional feature but new operation should\",\n \"map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) #\",\n \"1 return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if\",\n \"# Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also\",\n \"= Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel =\",\n \"= logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while\",\n \"utility that is shared across operations, possibly used from multiple threads, and the\",\n \"the old). The lock itself might not need to be built-in to Snapshotter,\",\n \"for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path =\",\n \"lock itself might not need to be built-in to Snapshotter, but having it\",\n \"already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared):\",\n \"contained in root object. Note that any call to public API MUST be\",\n \"old). The lock itself might not need to be built-in to Snapshotter, but\",\n \"in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None)\",\n \"dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst\",\n \"def _list_files(self, basepath: Path): result_files = set() for glob in self.globs: for path\",\n \"if self.src == self.dst: # The src=dst mode should be used if and\",\n \"if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r\",\n \"COULD also remove extra directories, but it is not # probably really worth\",\n \"assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path\",\n \"be made with snapshotter.lock held. This is because Snapshotter is process-wide utility that\",\n \"if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same\",\n \"progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD\",\n \"Path from typing import Optional import base64 import hashlib import logging import os\",\n \"i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if\",\n \"This happens only if snapshot is started twice at # same time. While\",\n \"known that files will not disappear between snapshot and # upload steps (e.g.\",\n \"lost - %s disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue if\",\n \"file list contained in root object. Note that any call to public API\",\n \"existing = 0 disappeared = 0 changes = 0 for i, relative_path in\",\n \"dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may\",\n \"other hashes which correspond to files referred to within the file list contained\",\n \"1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory:\",\n \"continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path)\",\n \"src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes\",\n \"any call to public API MUST be made with snapshotter.lock held. This is\",\n \"to ignored files it # actually might not even work. # Then, create/update\",\n \"# actually might not even work. # Then, create/update corresponding snapshotfile objects (old\",\n \"logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes += 1 return changes\",\n \"magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath)\",\n \"self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1)\",\n \"Optional import base64 import hashlib import logging import os import threading logger =\",\n \"snapshotter.lock held. This is because Snapshotter is process-wide utility that is shared across\",\n \"While it is technically speaking upstream # error, we rather handle it here\",\n \"happens only if snapshot is started twice at # same time. While it\",\n \"read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not data: break\",\n \"# Then, create/update corresponding snapshotfile objects (old # ones were already removed) dst_dirs,\",\n \"might not even work. # Then, create/update corresponding snapshotfile objects (old # ones\",\n \"in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost +=\",\n \"<= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*,\",\n \"relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes +=\",\n \"utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from\",\n \"import base64 import hashlib import logging import os import threading logger = logging.getLogger(__name__)\",\n \"present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <=\",\n \"SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest:\",\n \"= hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data =\",\n \"if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1 return changes\",\n \"logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash()\",\n \"path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for\",\n \"file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in\",\n \"increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes +=\",\n \"the lifecycle of the # files within). In that case, there is little\",\n \"= hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes\",\n \"for details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash,\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path =\",\n \"astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import\",\n \"logger.debug(\\\"#%d. same - %r in %s is same\\\", same, old_snapshotfile, relative_path) continue yield\",\n \"result_files = set() for glob in self.globs: for path in basepath.glob(glob): if not\",\n \"snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat()\",\n \"calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert globs # model\",\n \"_snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1):\",\n \"snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else:\",\n \"may or may not be present; dst is present as it is in\",\n \"operation can be started with old running' is intentional feature but new operation\",\n \"self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ]\",\n \"in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self,\",\n \"Snapshotter, but having it there enables asserting its state during public API calls.\",\n \"if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i\",\n \"== self.dst: # The src=dst mode should be used if and only if\",\n \"del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path\",\n \"snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out):\",\n \"before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file:\",\n \"disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path)\",\n \"The lock itself might not need to be built-in to Snapshotter, but having\",\n \"from pathlib import Path from typing import Optional import base64 import hashlib import\",\n \"not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing,\",\n \"dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing\",\n \"SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir\",\n \"# ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles))\",\n \"if it is # known that files will not disappear between snapshot and\",\n \"be used if and only if it is # known that files will\",\n \"if and only if it is # known that files will not disappear\",\n \"if snapshot is started twice at # same time. While it is technically\",\n \"Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove\",\n \"threads, and the single-operation-only mode of operation is not exactly flawless (the 'new\",\n \"while True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class\",\n \"globs # model has empty; either plugin or configuration must supply them self.src\",\n \"sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p\",\n \"# same time. While it is technically speaking upstream # error, we rather\",\n \"new file: %r\\\", i - disappeared, relative_path) changes += 1 return changes def\",\n \"dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs,\",\n \"we rather handle it here than leave # exceptions not handled. existing +=\",\n \"\\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from\",\n \"lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\",\",\n \"for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath)\",\n \"= 0 disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files),\",\n \"objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles =\",\n \"if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes\",\n \"*, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path\",\n \"progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially\",\n \"MAY change, which may require subsequent incremential snapshot and-or ignoring the files which\",\n \"1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost, self.src\",\n \"corresponding snapshotfile objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)\",\n \"_hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data\",\n \"across operations, possibly used from multiple threads, and the single-operation-only mode of operation\",\n \"ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 =\",\n \"== magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files =\",\n \"disappeared, relative_path) changes += 1 return changes def snapshot(self, *, progress: Optional[Progress] =\",\n \"not # probably really worth it and due to ignored files it #\",\n \"+= len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra\",\n \"+= 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost,\",\n \"def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in\",\n \"linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\",\",\n \"and the single-operation-only mode of operation is not exactly flawless (the 'new operation\",\n \"1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink()\",\n \"# Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files\",\n \"return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile:\",\n \"%r\\\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files):\",\n \"and their hashes. The hash on disk MAY change, which may require subsequent\",\n \"self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We\",\n \"in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent\",\n \"FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat,\",\n \"/ relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra\",\n \"ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s\",\n \"used from multiple threads, and the single-operation-only mode of operation is not exactly\",\n \"progress.start(1) if self.src == self.dst: # The src=dst mode should be used if\",\n \"dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or\",\n \"be built-in to Snapshotter, but having it there enables asserting its state during\",\n \"existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared\",\n \"just use the src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files\",\n \"[ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self):\",\n \"snapshotfile objects (old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles\",\n \"See LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import\",\n \"possibly used from multiple threads, and the single-operation-only mode of operation is not\",\n \"= old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if\",\n \"%r in %s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self):\",\n \"ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def\",\n \"= globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock =\",\n \"def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if\",\n \"SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing import\",\n \"it is not # probably really worth it and due to ignored files\",\n \"upstream # error, we rather handle it here than leave # exceptions not\",\n \"%s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if\",\n \"basepath: Path): result_files = set() for glob in self.globs: for path in basepath.glob(glob):\",\n \"Path): result_files = set() for glob in self.globs: for path in basepath.glob(glob): if\",\n \"= self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i,\",\n \"changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0 changes\",\n \"return changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress\",\n \"progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs)\",\n \"ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i\",\n \"return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0\",\n \"import increase_worth_reporting, Progress from pathlib import Path from typing import Optional import base64\",\n \"rather handle it here than leave # exceptions not handled. existing += 1\",\n \"self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self,\",\n \"is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size\",\n \"root object. Note that any call to public API MUST be made with\",\n \"None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self,\",\n \"from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib\",\n \"self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath =\",\n \"keeps track of files on disk, and their hashes. The hash on disk\",\n \"hash on disk MAY change, which may require subsequent incremential snapshot and-or ignoring\",\n \"def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path in relative_paths:\",\n \"only if snapshot is started twice at # same time. While it is\",\n \"lifecycle of the # files within). In that case, there is little point\",\n \"i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)\",\n \"dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\",\",\n \"self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def\",\n \"existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue\",\n \"Progress from pathlib import Path from typing import Optional import base64 import hashlib\",\n \"SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from\",\n \"# upload steps (e.g. Astacus controls the lifecycle of the # files within).\",\n \"their hashes. The hash on disk MAY change, which may require subsequent incremential\",\n \"new directory: %r\\\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *,\",\n \"def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs),\",\n \"(old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files))\",\n \"# making extra symlinks and we can just use the src # directory\",\n \"there enables asserting its state during public API calls. \\\"\\\"\\\" def __init__(self, *,\",\n \"Snapshotter is process-wide utility that is shared across operations, possibly used from multiple\",\n \"with snapshotter.lock held. This is because Snapshotter is process-wide utility that is shared\",\n \"changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress is\",\n \"twice at # same time. While it is technically speaking upstream # error,\",\n \"self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should be used\",\n \"exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\",\",\n \"speaking upstream # error, we rather handle it here than leave # exceptions\",\n \"at # same time. While it is technically speaking upstream # error, we\",\n \"old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if\",\n \"= 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path\",\n \"import hashlib import logging import os import threading logger = logging.getLogger(__name__) _hash =\",\n \"# src may or may not be present; dst is present as it\",\n \"running' is intentional feature but new operation should eventually replace the old). The\",\n \"= parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for\",\n \"changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0\",\n \"for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile =\",\n \"steps (e.g. Astacus controls the lifecycle of the # files within). In that\",\n \"self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1\",\n \"snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def\",\n \"snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src /\",\n \"operation is not exactly flawless (the 'new operation can be started with old\",\n \"SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for\",\n \"# exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed,\",\n \"path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name\",\n \"'new operation can be started with old running' is intentional feature but new\",\n \"single-operation-only mode of operation is not exactly flawless (the 'new operation can be\",\n \"= 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path\",\n \"from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress\",\n \"relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file:\",\n \"symlinks and we can just use the src # directory contents as-is. dst_dirs,\",\n \"\\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert globs # model has\",\n \"= self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile)\",\n \"= self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may\",\n \"directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files =\",\n \"= self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self,\",\n \"0 lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile\",\n \"files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if\",\n \"= 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path =\",\n \"_hash() while True: data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest()\",\n \"def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path,\",\n \"1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue if\",\n \"may require subsequent incremential snapshot and-or ignoring the files which have changed. The\",\n \"require subsequent incremential snapshot and-or ignoring the files which have changed. The output\",\n \"them self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {}\",\n \"1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i,\",\n \"parallel): assert globs # model has empty; either plugin or configuration must supply\",\n \"that is shared across operations, possibly used from multiple threads, and the single-operation-only\",\n \"for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile =\",\n \"src_path = self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path,\",\n \"- disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes += 1\",\n \"details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState\",\n \"with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest\",\n \"self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared\",\n \"lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile =\",\n \"in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d.\",\n \"try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is\",\n \"*, src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path\",\n \"correspond to files referred to within the file list contained in root object.\",\n \"src=dst mode should be used if and only if it is # known\",\n \"# files within). In that case, there is little point in # making\",\n \"built-in to Snapshotter, but having it there enables asserting its state during public\",\n \"and-or ignoring the files which have changed. The output to outside is just\",\n \"it here than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing):\",\n \"disappeared = 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path\",\n \"= threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in self.globs:\",\n \"self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set()\",\n \"except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before\",\n \"*, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if not data:\",\n \"disk MAY change, which may require subsequent incremential snapshot and-or ignoring the files\",\n \"= f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps\",\n \"increase_worth_reporting, Progress from pathlib import Path from typing import Optional import base64 import\",\n \"for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files)\",\n \"self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def\",\n \"should eventually replace the old). The lock itself might not need to be\",\n \"of files on disk, and their hashes. The hash on disk MAY change,\",\n \"is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return\",\n \"to Snapshotter, but having it there enables asserting its state during public API\",\n \"if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\", same, old_snapshotfile, relative_path)\",\n \"dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot is started twice\",\n \"have changed. The output to outside is just root object's hash, as well\",\n \"than leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s\",\n \"files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files,\",\n \"LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile,\",\n \"well as list of other hashes which correspond to files referred to within\",\n \"worth it and due to ignored files it # actually might not even\",\n \"in root object. Note that any call to public API MUST be made\",\n \"\\\"\\\"\\\" Copyright (c) 2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common\",\n \"astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import\",\n \"_list_files(self, basepath: Path): result_files = set() for glob in self.globs: for path in\",\n \"continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile:\",\n \"lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64\",\n \"{} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files =\",\n \"same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\",\",\n \"model has empty; either plugin or configuration must supply them self.src = Path(src)\",\n \"actually might not even work. # Then, create/update corresponding snapshotfile objects (old #\",\n \"utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success() return\",\n \"def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] =\",\n \"_list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p in files}\",\n \"i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes\",\n \"old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in\",\n \"making extra symlinks and we can just use the src # directory contents\",\n \"= src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories\",\n \"data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on\",\n \"mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path\",\n \"dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We\",\n \"in # making extra symlinks and we can just use the src #\",\n \"multiple threads, and the single-operation-only mode of operation is not exactly flawless (the\",\n \"snapshot and-or ignoring the files which have changed. The output to outside is\",\n \"increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared +=\",\n \"relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost\",\n \"mode should be used if and only if it is # known that\",\n \"hashes. The hash on disk MAY change, which may require subsequent incremential snapshot\",\n \"contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)\",\n \"+= self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but it\",\n \"output to outside is just root object's hash, as well as list of\",\n \"get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if\",\n \"st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0\",\n \"if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents:\",\n \"(the 'new operation can be started with old running' is intentional feature but\",\n \"- %s disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile:\",\n \"= snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] ==\",\n \"and we can just use the src # directory contents as-is. dst_dirs, dst_files\",\n \"import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting,\",\n \"import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *,\",\n \"self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if\",\n \"disk, and their hashes. The hash on disk MAY change, which may require\",\n \"the # files within). In that case, there is little point in #\",\n \"= base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out)\",\n \"disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest =\",\n \"logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self,\",\n \"h = _hash() while True: data = f.read(read_buffer) if not data: break h.update(data)\",\n \"really worth it and due to ignored files it # actually might not\",\n \"src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path =\",\n \"Copyright (c) 2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import\",\n \"and due to ignored files it # actually might not even work. #\",\n \"it # actually might not even work. # Then, create/update corresponding snapshotfile objects\",\n \"= list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be present;\",\n \"1): src_path = self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path,\",\n \"same - %r in %s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile\",\n \"self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob in\",\n \"if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared\",\n \"to outside is just root object's hash, as well as list of other\",\n \"src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode\",\n \"for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError:\",\n \"flawless (the 'new operation can be started with old running' is intentional feature\",\n \"SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing\",\n \"itself might not need to be built-in to Snapshotter, but having it there\",\n \"src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def\",\n \"self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if\",\n \"increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\", same, old_snapshotfile, relative_path) continue\",\n \"extra directories, but it is not # probably really worth it and due\",\n \"dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This\",\n \"not be present; dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst)\",\n \"src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should\",\n \"return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for\",\n \"self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir)\",\n \"i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing\",\n \"break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk,\",\n \"even work. # Then, create/update corresponding snapshotfile objects (old # ones were already\",\n \"= self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode should be\",\n \"not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files\",\n \"which correspond to files referred to within the file list contained in root\",\n \"1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared =\",\n \"in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode()\",\n \"snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True\",\n \"= self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i,\",\n \"relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst /\",\n \"is little point in # making extra symlinks and we can just use\",\n \"get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes =\",\n \"relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for\",\n \"in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst / relative_path\",\n \"is process-wide utility that is shared across operations, possibly used from multiple threads,\"\n]","isConversational":false}}},{"rowIdx":3499,"cells":{"ngram":{"kind":"list like","value":["raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task (by","return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install')","metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options must","# invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def","from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from","pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError","in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context,","dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir,","os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',","if additional_hooks is None: additional_hooks = [] # add jars and classes to","start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames)","not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src')","= os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames,","cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg #","os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path))","shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the build one","+= (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment in","= dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in","GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def","in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *,","args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on the","src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):","def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass","Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd +=","'*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend')","in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory","create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await","for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames:","async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg =","# add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks +=","in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames,","subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames,","bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames -","# Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None:","on the destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base,","Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add","import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): #","import glob import os from pathlib import Path import shutil from colcon_core.environment import","cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment","def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on the","cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise","= [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") #","Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base","get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class","= self.context.pkg args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks","from distutils import dir_util import glob import os from pathlib import Path import","classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name,","Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build',","specific task instead of the default task') async def build( # noqa: D102","or []) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX']","destination tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir","'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames =","[args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or [])","cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd +=","+= [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or","src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames -","destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs')","src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir)","_build(self, args, env): self.progress('build') # remove anything on the destination tree but not","build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name,","get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc =","'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir)","os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa:","= [] # add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762","'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes',","pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger","noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames =","self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \"Building","= dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames:","src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for","2.0 from distutils import dir_util import glob import os from pathlib import Path","os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in","additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env","Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd =","- src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if","pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if","noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args","but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir =","dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename","self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await self._install(args, env)","elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not","= [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError(","start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args',","env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base,","os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar,","not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or","build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args,","'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'),","additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks","os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames","colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell","to avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1)","start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path):","super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for","install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return","dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname,","in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname))","from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from","create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint","import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import","colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment","Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd","GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find","(out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*',","_build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in","for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the","is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle'","async def _build(self, args, env): self.progress('build') # remove anything on the destination tree","args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames","[GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle","bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames =","out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames:","logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = []","the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd =","remove anything on the destination tree but not in the source tree src_package_src_dir","to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await","# remove anything on the destination tree but not in the source tree","arguments to Gradle projects. ' 'Arguments matching other options must be prefixed by","prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname","= self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames","\"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] #","for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname,","has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else:","RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task (by default","rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async","to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH',","None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper'","dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree(","self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg,","for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(","'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as","return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args,","in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) #","os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files","prune_dirname)) # copy files from the source directory to the build one to","import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import","default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle","set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in","rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and","if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks)","not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') #","args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None:","prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source","one to avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base,","executable\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else:","import os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from","os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return","+= ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base #","+= create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env =","from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from","import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import","on the destination tree but not in the source tree src_package_src_dir = os.path.join(args.path,","env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return","during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd","is None: additional_hooks = [] # add jars and classes to CLASSPATH with","= set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add(","gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path):","= set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname","# remove anything on the destination tree but not in the build tree","parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching","mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try:","if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd","the Apache License, Version 2.0 from distutils import dir_util import glob import os","out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename),","2018 <NAME> # Licensed under the Apache License, Version 2.0 from distutils import","# polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle","but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base,","by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task","'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames","nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options","'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames =","additional_hooks = [] # add jars and classes to CLASSPATH with wildcards #","self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for","'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames =","[]) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] =","under the Apache License, Version 2.0 from distutils import dir_util import glob import","import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import","env): self.progress('build') # remove anything on the destination tree but not in the","RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc","invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self,","<NAME> # Licensed under the Apache License, Version 2.0 from distutils import dir_util","= os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir)","rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env):","os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames","prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename =","= self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames","import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle","os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except","copy files from the source directory to the build one to avoid #","tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree(","pkg = self.context.pkg # remove anything on the destination tree but not in","colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self):","if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the","logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107","filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self,","colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle","'^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames,","[str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could","a specific task instead of the default task') async def build( # noqa:","try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e))","has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa:","= args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env)","import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import","'--gradle-task', help='Run a specific task instead of the default task') async def build(","create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything","if rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc","from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import","prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to","self.progress('install') pkg = self.context.pkg # remove anything on the destination tree but not","wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java',","# copy files from the source directory to the build one to avoid","+= ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace']","and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def","dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree(","'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment(","import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import","be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a","= os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True)","find 'gradle' or 'wrapper' executable\") # Gradle Task (by default 'assemble') if args.gradle_task:","satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run","files from the source directory to the build one to avoid # polluting","bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir,","rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return","logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and rc.returncode: return","src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames","for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir,","additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on the destination","= self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames","exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames =","to Gradle projects. ' 'Arguments matching other options must be prefixed by a","= os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir)","or 'wrapper' executable\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd +=","(by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] #","self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames","self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env)","bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames","avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) #","prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from","out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): #","+= create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks +=","await self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await self._install(args,","colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle","D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments","): pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals()))","prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in","*, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to","'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies)","default task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ):","for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir,","e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and rc.returncode:","'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH',","must be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run","await self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts(","<filename>colcon_gradle/task/gradle/build.py<gh_stars>0 # Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0","import dir_util import glob import os from pathlib import Path import shutil from","cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args","dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE","with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,","except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if","in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename","= await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1","not find 'gradle' or 'wrapper' executable\") # Gradle Task (by default 'assemble') if","help='Pass arguments to Gradle projects. ' 'Arguments matching other options must be prefixed","from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from","rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode if","prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific","tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java')","dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname),","= colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__()","the destination tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src')","Version 2.0 from distutils import dir_util import glob import os from pathlib import","glob import os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts","the source directory to the build one to avoid # polluting the latter","# Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd","os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging","pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on","self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks =","anything on the destination tree but not in the source tree src_package_src_dir =","def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg","TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable","from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger","for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def","= dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames:","to the build one to avoid # polluting the latter during the build","out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip,","the build one to avoid # polluting the latter during the build process","the destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build',","prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')):","'wrapper' executable\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task]","if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build')","\"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self,","__init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set()","dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename","= self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks","dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames","colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger =","prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname))","set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath(","dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path))","Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step","prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in","filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for","src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames =","args, env): self.progress('install') pkg = self.context.pkg # remove anything on the destination tree","from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from","pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base,","class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')","' 'Arguments matching other options must be prefixed by a space,\\n' 'e.g. --gradle-args","dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames","env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args,","parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task') async def","'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames =","in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename","build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())]","distutils import dir_util import glob import os from pathlib import Path import shutil","filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument(","self.progress('build') # remove anything on the destination tree but not in the source","os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102","import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import","env) if rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if","import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__)","_install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on the destination","async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on","rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode if not","pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name,","prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy","def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames =","prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for","'Arguments matching other options must be prefixed by a space,\\n' 'e.g. --gradle-args \"","remove anything on the destination tree but not in the build tree bld_package_jar_dir","shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir,","[] # add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks","dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir,","latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args):","'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the","return rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode","colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task","'--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other","colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell","# Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build","filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser):","directory to the build one to avoid # polluting the latter during the","pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree(","# Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add","# noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames","colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION,","prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname","dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames:","env): self.progress('install') pkg = self.context.pkg # remove anything on the destination tree but","in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base,","cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove","dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames -","parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle","the default task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False","satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname,","*, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle","await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc","D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set()","run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE","# noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args =","return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*',","CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',","update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not","['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] #","pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,","else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd","the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames","self.context.pkg args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is","os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames,","# Licensed under the Apache License, Version 2.0 from distutils import dir_util import","the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if","task instead of the default task') async def build( # noqa: D102 self,","'gradle' or 'wrapper' executable\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd","matching other options must be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"')","not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join(","None: additional_hooks = [] # add jars and classes to CLASSPATH with wildcards","process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif","License, Version 2.0 from distutils import dir_util import glob import os from pathlib","def _build(self, args, env): self.progress('build') # remove anything on the destination tree but","rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode rc =","env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything","step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env):","= self.context.pkg # remove anything on the destination tree but not in the","help='Run a specific task instead of the default task') async def build( #","get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable","anything on the destination tree but not in the build tree bld_package_jar_dir =","self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg","os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames","run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg =","self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames","https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend')","skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle package in","GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\"","['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke","1 rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode rc","Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system","def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames","prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in","tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir","os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the build","Apache License, Version 2.0 from distutils import dir_util import glob import os from","other options must be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument(","build one to avoid # polluting the latter during the build process dir_util.copy_tree(args.path,","polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable","bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for","task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg","rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self,","from the source directory to the build one to avoid # polluting the","dir_util import glob import os from pathlib import Path import shutil from colcon_core.environment","args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is","add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments","# noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects.","in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars and","cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\")","as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and","prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir,","dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames -","and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base),","build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args","= dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in","args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args,","os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir, jar_filename))","colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build","os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar","space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of","- bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if","source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames =","args, env): self.progress('build') # remove anything on the destination tree but not in","shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version","\"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task (by default 'assemble')","create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook(","from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from","noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. '","environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run(","from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from","args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async","\" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task')","- bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename))","GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context, cmd,","args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd +=","instead of the default task') async def build( # noqa: D102 self, *,","type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options must be","(args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX","skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove","'{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars and classes","packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames","Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to","self.context.pkg # remove anything on the destination tree but not in the build","= await self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation:","self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for","Licensed under the Apache License, Version 2.0 from distutils import dir_util import glob","# https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'),","await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg","--help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task') async","bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):","cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base","source directory to the build one to avoid # polluting the latter during","out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for","dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames:","Gradle projects. ' 'Arguments matching other options must be prefixed by a space,\\n'","of the default task') async def build( # noqa: D102 self, *, additional_hooks=None,","import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import","projects. ' 'Arguments matching other options must be prefixed by a space,\\n' 'e.g.","'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await","if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE]","= self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames","jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars',","colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle","os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames,","options must be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task',","from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint):","Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0 from distutils","from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def","a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead","add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook(","and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and rc.returncode:","the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share',","return 1 rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode","additional_hooks is None: additional_hooks = [] # add jars and classes to CLASSPATH","tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir =","mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e:","Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base),","in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath(","subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add(","additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle package","else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task","# Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0 from","Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble']","create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook","if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar)","- src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename))","= await self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await","colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task","D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info(","--gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default","import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from","package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars","'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments"],"string":"[\n \"raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by\",\n \"return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install')\",\n \"metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options must\",\n \"# invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def\",\n \"from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from\",\n \"pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError\",\n \"in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context,\",\n \"dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir,\",\n \"os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',\",\n \"if additional_hooks is None: additional_hooks = [] # add jars and classes to\",\n \"start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames)\",\n \"not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src')\",\n \"= os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames,\",\n \"cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg #\",\n \"os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path))\",\n \"shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the build one\",\n \"+= (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment in\",\n \"= dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in\",\n \"GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def\",\n \"in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *,\",\n \"args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on the\",\n \"src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):\",\n \"def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass\",\n \"Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd +=\",\n \"'*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend')\",\n \"in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory\",\n \"create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await\",\n \"for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames:\",\n \"async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg =\",\n \"# add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks +=\",\n \"in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames,\",\n \"subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames,\",\n \"bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames -\",\n \"# Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None:\",\n \"on the destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base,\",\n \"Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add\",\n \"import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): #\",\n \"import glob import os from pathlib import Path import shutil from colcon_core.environment import\",\n \"cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment\",\n \"def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on the\",\n \"cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise\",\n \"= [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") #\",\n \"Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base\",\n \"get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class\",\n \"= self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks\",\n \"from distutils import dir_util import glob import os from pathlib import Path import\",\n \"classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name,\",\n \"Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build',\",\n \"specific task instead of the default task') async def build( # noqa: D102\",\n \"or []) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX']\",\n \"destination tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir\",\n \"'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames =\",\n \"[args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or [])\",\n \"cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd +=\",\n \"+= [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or\",\n \"src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames -\",\n \"destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs')\",\n \"src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir)\",\n \"_build(self, args, env): self.progress('build') # remove anything on the destination tree but not\",\n \"build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name,\",\n \"get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc =\",\n \"'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir)\",\n \"os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa:\",\n \"= [] # add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762\",\n \"'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes',\",\n \"pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger\",\n \"noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames =\",\n \"self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building\",\n \"= dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames:\",\n \"src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for\",\n \"2.0 from distutils import dir_util import glob import os from pathlib import Path\",\n \"os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in\",\n \"additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env\",\n \"Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd =\",\n \"- src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if\",\n \"pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if\",\n \"noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args\",\n \"but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir =\",\n \"dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename\",\n \"self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await self._install(args, env)\",\n \"elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not\",\n \"= [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError(\",\n \"start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args',\",\n \"env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base,\",\n \"os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar,\",\n \"not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or\",\n \"build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args,\",\n \"'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'),\",\n \"additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks\",\n \"os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames\",\n \"colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell\",\n \"to avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1)\",\n \"start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path):\",\n \"super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for\",\n \"install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return\",\n \"dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname,\",\n \"in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname))\",\n \"from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from\",\n \"create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint\",\n \"import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import\",\n \"colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment\",\n \"Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd\",\n \"GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find\",\n \"(out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*',\",\n \"_build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in\",\n \"for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the\",\n \"is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle'\",\n \"async def _build(self, args, env): self.progress('build') # remove anything on the destination tree\",\n \"args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames\",\n \"[GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle\",\n \"bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames =\",\n \"out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames:\",\n \"logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = []\",\n \"the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd =\",\n \"remove anything on the destination tree but not in the source tree src_package_src_dir\",\n \"to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await\",\n \"# remove anything on the destination tree but not in the source tree\",\n \"arguments to Gradle projects. ' 'Arguments matching other options must be prefixed by\",\n \"prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname\",\n \"= self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames\",\n \"\\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] #\",\n \"for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname,\",\n \"has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else:\",\n \"RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by default\",\n \"rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async\",\n \"to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH',\",\n \"None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper'\",\n \"dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree(\",\n \"self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg,\",\n \"for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(\",\n \"'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as\",\n \"return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args,\",\n \"in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) #\",\n \"os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files\",\n \"prune_dirname)) # copy files from the source directory to the build one to\",\n \"import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import\",\n \"default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle\",\n \"set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in\",\n \"rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and\",\n \"if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks)\",\n \"not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') #\",\n \"args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None:\",\n \"prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source\",\n \"one to avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base,\",\n \"executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else:\",\n \"import os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from\",\n \"os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return\",\n \"+= ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base #\",\n \"+= create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env =\",\n \"from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from\",\n \"import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import\",\n \"on the destination tree but not in the source tree src_package_src_dir = os.path.join(args.path,\",\n \"env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return\",\n \"during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd\",\n \"is None: additional_hooks = [] # add jars and classes to CLASSPATH with\",\n \"= set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add(\",\n \"gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path):\",\n \"= set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname\",\n \"# remove anything on the destination tree but not in the build tree\",\n \"parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching\",\n \"mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try:\",\n \"if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd\",\n \"the Apache License, Version 2.0 from distutils import dir_util import glob import os\",\n \"out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename),\",\n \"2018 <NAME> # Licensed under the Apache License, Version 2.0 from distutils import\",\n \"# polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle\",\n \"but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base,\",\n \"by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task\",\n \"'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames\",\n \"nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options\",\n \"'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames =\",\n \"additional_hooks = [] # add jars and classes to CLASSPATH with wildcards #\",\n \"self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for\",\n \"'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames =\",\n \"[]) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] =\",\n \"under the Apache License, Version 2.0 from distutils import dir_util import glob import\",\n \"import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import\",\n \"env): self.progress('build') # remove anything on the destination tree but not in the\",\n \"RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc\",\n \"invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self,\",\n \"<NAME> # Licensed under the Apache License, Version 2.0 from distutils import dir_util\",\n \"= os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir)\",\n \"rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env):\",\n \"os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames\",\n \"prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename =\",\n \"= self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames\",\n \"import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle\",\n \"os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except\",\n \"copy files from the source directory to the build one to avoid #\",\n \"tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree(\",\n \"pkg = self.context.pkg # remove anything on the destination tree but not in\",\n \"colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self):\",\n \"if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the\",\n \"logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107\",\n \"filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self,\",\n \"colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle\",\n \"'^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames,\",\n \"[str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could\",\n \"a specific task instead of the default task') async def build( # noqa:\",\n \"try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e))\",\n \"has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa:\",\n \"= args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env)\",\n \"import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import\",\n \"'--gradle-task', help='Run a specific task instead of the default task') async def build(\",\n \"create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything\",\n \"if rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc\",\n \"from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import\",\n \"prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to\",\n \"self.progress('install') pkg = self.context.pkg # remove anything on the destination tree but not\",\n \"wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java',\",\n \"# copy files from the source directory to the build one to avoid\",\n \"+= ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace']\",\n \"and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def\",\n \"dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree(\",\n \"'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment(\",\n \"import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import\",\n \"be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a\",\n \"= os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True)\",\n \"find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task:\",\n \"satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run\",\n \"files from the source directory to the build one to avoid # polluting\",\n \"bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir,\",\n \"rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return\",\n \"logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and rc.returncode: return\",\n \"src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames\",\n \"for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir,\",\n \"additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on the destination\",\n \"= self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames\",\n \"exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames =\",\n \"to Gradle projects. ' 'Arguments matching other options must be prefixed by a\",\n \"= os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir)\",\n \"or 'wrapper' executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd +=\",\n \"(by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] #\",\n \"self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames\",\n \"self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env)\",\n \"bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames\",\n \"avoid # polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) #\",\n \"prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from\",\n \"out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): #\",\n \"+= create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks +=\",\n \"await self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await self._install(args,\",\n \"colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle\",\n \"D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments\",\n \"): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals()))\",\n \"prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in\",\n \"*, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to\",\n \"'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies)\",\n \"default task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ):\",\n \"for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir,\",\n \"e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and rc.returncode:\",\n \"'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH',\",\n \"must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run\",\n \"await self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts(\",\n \"<filename>colcon_gradle/task/gradle/build.py<gh_stars>0 # Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0\",\n \"import dir_util import glob import os from pathlib import Path import shutil from\",\n \"cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args\",\n \"dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE\",\n \"with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,\",\n \"except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if\",\n \"in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename\",\n \"= await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1\",\n \"not find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by default 'assemble') if\",\n \"help='Pass arguments to Gradle projects. ' 'Arguments matching other options must be prefixed\",\n \"from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from\",\n \"rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode if\",\n \"prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific\",\n \"tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java')\",\n \"dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname),\",\n \"= colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__()\",\n \"the destination tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src')\",\n \"Version 2.0 from distutils import dir_util import glob import os from pathlib import\",\n \"glob import os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts\",\n \"the source directory to the build one to avoid # polluting the latter\",\n \"# Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd\",\n \"os from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging\",\n \"pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything on\",\n \"self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks =\",\n \"anything on the destination tree but not in the source tree src_package_src_dir =\",\n \"def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg\",\n \"TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable\",\n \"from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger\",\n \"for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def\",\n \"= dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames:\",\n \"to the build one to avoid # polluting the latter during the build\",\n \"out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip,\",\n \"the build one to avoid # polluting the latter during the build process\",\n \"the destination tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build',\",\n \"prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')):\",\n \"'wrapper' executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task]\",\n \"if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build')\",\n \"\\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self,\",\n \"__init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set()\",\n \"dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename\",\n \"= self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks\",\n \"dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames\",\n \"colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger =\",\n \"prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname))\",\n \"set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath(\",\n \"dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path))\",\n \"Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step\",\n \"prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in\",\n \"filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for\",\n \"src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames =\",\n \"args, env): self.progress('install') pkg = self.context.pkg # remove anything on the destination tree\",\n \"from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from\",\n \"pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base,\",\n \"class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')\",\n \"' 'Arguments matching other options must be prefixed by a space,\\\\n' 'e.g. --gradle-args\",\n \"dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames\",\n \"env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args,\",\n \"parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task') async def\",\n \"'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames =\",\n \"in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename\",\n \"build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())]\",\n \"distutils import dir_util import glob import os from pathlib import Path import shutil\",\n \"filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument(\",\n \"self.progress('build') # remove anything on the destination tree but not in the source\",\n \"os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102\",\n \"import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import\",\n \"env) if rc and rc.returncode: return rc.returncode rc = await self._install(args, env) if\",\n \"import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__)\",\n \"_install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on the destination\",\n \"async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything on\",\n \"rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode if not\",\n \"pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name,\",\n \"prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy\",\n \"def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames =\",\n \"prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for\",\n \"'Arguments matching other options must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\"\",\n \"remove anything on the destination tree but not in the build tree bld_package_jar_dir\",\n \"shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir,\",\n \"[] # add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks\",\n \"dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir,\",\n \"latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args):\",\n \"'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the\",\n \"return rc.returncode rc = await self._install(args, env) if rc and rc.returncode: return rc.returncode\",\n \"colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task\",\n \"'--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other\",\n \"colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell\",\n \"# Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build\",\n \"filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser):\",\n \"directory to the build one to avoid # polluting the latter during the\",\n \"pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree(\",\n \"# Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add\",\n \"# noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames\",\n \"colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION,\",\n \"prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname\",\n \"dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames:\",\n \"env): self.progress('install') pkg = self.context.pkg # remove anything on the destination tree but\",\n \"in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base,\",\n \"cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove\",\n \"dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames -\",\n \"parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle\",\n \"the default task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False\",\n \"satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname,\",\n \"*, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle\",\n \"await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc\",\n \"D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set()\",\n \"run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE\",\n \"# noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args =\",\n \"return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*',\",\n \"CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',\",\n \"update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not\",\n \"['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] #\",\n \"pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,\",\n \"else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd\",\n \"the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames\",\n \"self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is\",\n \"os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames,\",\n \"# Licensed under the Apache License, Version 2.0 from distutils import dir_util import\",\n \"the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if\",\n \"task instead of the default task') async def build( # noqa: D102 self,\",\n \"'gradle' or 'wrapper' executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd\",\n \"matching other options must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"')\",\n \"not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join(\",\n \"None: additional_hooks = [] # add jars and classes to CLASSPATH with wildcards\",\n \"process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif\",\n \"License, Version 2.0 from distutils import dir_util import glob import os from pathlib\",\n \"def _build(self, args, env): self.progress('build') # remove anything on the destination tree but\",\n \"rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode rc =\",\n \"env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything\",\n \"step return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env):\",\n \"= self.context.pkg # remove anything on the destination tree but not in the\",\n \"help='Run a specific task instead of the default task') async def build( #\",\n \"get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable\",\n \"anything on the destination tree but not in the build tree bld_package_jar_dir =\",\n \"self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg\",\n \"os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames\",\n \"run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg =\",\n \"self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames\",\n \"https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend')\",\n \"skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in\",\n \"GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\"\",\n \"['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke\",\n \"1 rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode rc\",\n \"Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system\",\n \"def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames\",\n \"prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in\",\n \"tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir\",\n \"os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to the build\",\n \"Apache License, Version 2.0 from distutils import dir_util import glob import os from\",\n \"other options must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument(\",\n \"build one to avoid # polluting the latter during the build process dir_util.copy_tree(args.path,\",\n \"polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable\",\n \"bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for\",\n \"task') async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg\",\n \"rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self,\",\n \"from the source directory to the build one to avoid # polluting the\",\n \"dir_util import glob import os from pathlib import Path import shutil from colcon_core.environment\",\n \"args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is\",\n \"add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments\",\n \"# noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects.\",\n \"in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars and\",\n \"cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\")\",\n \"as e: logger.error(str(e)) return 1 rc = await self._build(args, env) if rc and\",\n \"prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir,\",\n \"dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames -\",\n \"and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base),\",\n \"build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args\",\n \"= dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in\",\n \"args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args,\",\n \"os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir, jar_filename))\",\n \"colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build\",\n \"os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar\",\n \"space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of\",\n \"- bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if\",\n \"source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames =\",\n \"args, env): self.progress('build') # remove anything on the destination tree but not in\",\n \"shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version\",\n \"\\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by default 'assemble')\",\n \"create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook(\",\n \"from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from\",\n \"noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. '\",\n \"environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run(\",\n \"from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from\",\n \"args.install_base # invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env) async\",\n \"\\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task')\",\n \"- bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename))\",\n \"GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run( self.context, cmd,\",\n \"args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd +=\",\n \"instead of the default task') async def build( # noqa: D102 self, *,\",\n \"type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other options must be\",\n \"(args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX\",\n \"skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove\",\n \"'{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars and classes\",\n \"packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames\",\n \"Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to\",\n \"self.context.pkg # remove anything on the destination tree but not in the build\",\n \"= await self._install(args, env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation:\",\n \"self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for\",\n \"Licensed under the Apache License, Version 2.0 from distutils import dir_util import glob\",\n \"# https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'),\",\n \"await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg\",\n \"--help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default task') async\",\n \"bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):\",\n \"cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base\",\n \"source directory to the build one to avoid # polluting the latter during\",\n \"out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for\",\n \"dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames:\",\n \"Gradle projects. ' 'Arguments matching other options must be prefixed by a space,\\\\n'\",\n \"of the default task') async def build( # noqa: D102 self, *, additional_hooks=None,\",\n \"import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import\",\n \"projects. ' 'Arguments matching other options must be prefixed by a space,\\\\n' 'e.g.\",\n \"'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc = await\",\n \"if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE]\",\n \"= self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames\",\n \"jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars',\",\n \"colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle\",\n \"os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames,\",\n \"options must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task',\",\n \"from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint):\",\n \"Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0 from distutils\",\n \"from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def\",\n \"a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead\",\n \"add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook(\",\n \"and rc.returncode: return rc.returncode rc = await self._install(args, env) if rc and rc.returncode:\",\n \"the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share',\",\n \"return 1 rc = await self._build(args, env) if rc and rc.returncode: return rc.returncode\",\n \"additional_hooks is None: additional_hooks = [] # add jars and classes to CLASSPATH\",\n \"tree but not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir =\",\n \"mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e:\",\n \"Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base),\",\n \"in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath(\",\n \"subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add(\",\n \"additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package\",\n \"else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle Task\",\n \"# Copyright 2018 <NAME> # Licensed under the Apache License, Version 2.0 from\",\n \"Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble']\",\n \"create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook\",\n \"if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar)\",\n \"- src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename))\",\n \"= await self._build(args, env) if rc and rc.returncode: return rc.returncode rc = await\",\n \"colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task\",\n \"D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info(\",\n \"--gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the default\",\n \"import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from\",\n \"package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add jars\",\n \"'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":34,"numItemsPerPage":100,"numTotalItems":12866649,"offset":3400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzUzMTY5NiwianRpIjoiMjRhOWZhN2UtYmZkZC00MWRmLTgwMTgtZjk2YmFhZjNmMzI5Iiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzUzNTI5NiwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9._8FCrvmSZ9j2DF7H6IdteiwVsfH6-pnsV7y9064Ij3Z5imE7_dXG7kBoqHcPNe9tC9pickeX2hfiQ68D-FB6Cg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
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[
"\"applicant_id\": app.ref_id}) row += [vemp.status] else: row += [\"-\"] if cg.address_check4 == 1:",
"emp = frappe.get_doc(\"Family Check1\", { \"applicant_id\": app.ref_id}) if emp.status != \"Allocation Completed\": row",
"emp = frappe.get_doc(\"Reference Check1\", { \"applicant_id\": app.ref_id}) if emp.st... |
[
"register(name): def decorator(cls): datasets_dt[name] = cls return cls return decorator def make(name, **kwargs):",
"def decorator(cls): datasets_dt[name] = cls return cls return decorator def make(name, **kwargs): if",
"{} def register(name): def decorator(cls): datasets_dt[name] = cls return cls return decor... |
[
"Protobuf from .proto_structures import Syntax class SyntaxParser(): @classmethod def parse_and_add(cls, proto_obj: Protobuf, line,",
"not None: raise 'multiple syntax detected!' proto_obj.syntax = cls.parse_syntax(line, top_comment_list) @classmethod def parse_syntax(cls,",
"@classmethod def parse_syntax(cls, ... |
[
"return render_template('about.html') @core.route('/search', methods=['GET', 'POST']) def search(): search_str = request.args.get('globalsearch') return render_template('search.html', search_str=search_str)",
"Blueprint('core', __name__) @core.route(\"/\", methods=['GET', 'POST']) def home(): return render_templa... |
[
"( \"with_overdrive_data_source_and_scaled_size_and_custom_region\", \"test-book-covers-s3-bucket\", DataSource.OVERDRIVE, \"https://test-book-covers-s3-bucket.s3.us-east-3.amazonaws.com/scaled/300/Overdrive/\", 300, \"us-east-3\", ), ] ) def test_cover_image_root( self,",
"!= \"\" else None) assert \"config\" no... |
[
"= os.path.dirname(os.path.dirname(os.path.realpath(__file__))) build_type_path = os.path.join(root_dir, 'lbry', 'build_type.py') log.debug(\"configuring build type file: %s\", build_type_path)",
"return 'release' return 'qa' def main(): root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) buil... |
[
"= [] testplan_dict[\"peernodeAlias\"] =[] if os.path.exists(work_dir) != True: print 'certs keyfiles directory do",
"install_list = [] for org in org_list: channel_join = template[\"CHANNEL_JOIN\"] channel_join[\"parameters\"][\"connectionProfile\"] = org",
"from collections import OrderedDict curr_dir = os.ge... |
[
"unplugged.') f2 = dblib.lib.Finding('Monitor switched off', 'my_PC', 'The monitor is switched off.') def",
"in db.findings assert len(db.findings) == 1 db.add(f1) assert f1 in db.findings assert len(db.findings)",
"f2 = dblib.lib.Finding('Monitor switched off', 'my_PC', 'The monitor is switched off.') def test... |
[
"elif management_group: result = client.remediations.list_for_management_group(management_group_id=management_group) else: result = client.remediations.list_for_subscription(subscription_id=sub) return [i.name for i",
"import QueryOptions query_options = QueryOptions(top=2000) return [metadata.name for metadata i... |
[
"\"mm/yy/dd\"), (\"%m.%y.%d\", \"mm.yy.dd\"), (\"%y-%d-%m\", \"yy-dd-mm\"), (\"%y/%d/%m\", \"yy/dd/mm\"), (\"%y.%d.%m\", \"yy.dd.mm\"), (\"%y-%m-%d\", \"yy-mm-dd\"), (\"%y/%m/%d\", \"yy/mm/dd\"),",
"models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name=\"columns\", to=\"hordak.TransactionIm... |
[
"Dispatcher(bot) @dp.message_handler(command=['start', 'help']) async def send_welcome(msg: types.Message): await msg.reply_to_message(f'Добро пожаловать,{msg.from_user.first_name}') @dp.message_handler(content_types=['text']) async def",
"types.Message): await msg.reply_to_message(f'Добро пожаловать,{msg.from_us... |
[
"False return True class LazyEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, Promise): return force_text(obj)",
"import import_module try: from django.utils.encoding import force_text except ImportError: from django.utils.encoding import force_unicode",
"path.\".format(path) raise Improper... |
[
"self._lastCmdTime # Set last time method was called to current time self._lastCmdTime =",
"= 0.): \"\"\"Sets the proportional, integral and derivative terms.\"\"\" self._kp = kp self._ki",
"TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.",
"self._cmdMax = cmdMax ... |
[
"= message['from'] attachments.append(attachment) return slack_payload def _report_message_to_slack(self, message): \"\"\" Send a message to",
"self.cfg['pmon']['slack.hook'] payload = json.dumps(self._make_slack_payload(message)) headers = {'Accept': 'application/json', 'Content-Type': 'application/json', 'Conte... |
[
"before, after) == after[i] \"\"\" def test_ti(): assert S.ti(\"ta\", \"e\") == \"te\" assert",
"assert S.ti(\"AtAm\", \"e\") == \"Ate\" def test_antya(): assert S.antya(\"ti\", \"u\") == \"tu\" assert",
"\"tam\", \"ta\", \"am\") for i, b in enumerate(before): assert op.yatha(b, before, after) ==",
"def test_... |
[
"emails = TVSMembers[TVSMembers.primary == primary]['email'].values print (\"These are the members with primary affiliation",
"you also want their names and affiliations use: \") print (\"$python extractemailsW.py \"",
"json filename parser = ArgumentParser(description='Selecting members by subgroup') parser.ad... |
[
"ctx.send(\"You can't remove None\") cur = await self.bot.sus_users.cursor() await cur.execute(\"DELETE FROM RTFM_DICTIONARY WHERE",
"user.send(embed=embed_message) except: user = ctx.author await user.send(content=\"Message failed. sending\",embed=embed_message) embed_message.add_field(name=\"Sent To:\",value=st... |
[
"= 'sec', arguments = ('z',), expression = '1./cmath.cos(z)') asec = Function(name = 'asec',",
"is part of the UFO. # # This file contains definitions for functions",
"# shortcuts for functions from cmath # complexconjugate = Function(name = 'complexconjugate', arguments",
"= ('z',), expression = '1./cmath.co... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"config = cls.get_config_or_default() return config.check_count + 1 @classmethod def reset( cls, check_count=None, current_version=None,",
"KIND, e... |
[
"duckql.properties import Null @pytest.fixture(scope=\"module\") def valid_instance() -> Null: return Null() def test_string(valid_instance: Null):",
"test_string(valid_instance: Null): assert str(valid_instance) == 'NULL' def test_obj(valid_instance: Null): assert valid_instance.obj == 'properties.Null'",
"-> ... |
[
"if (isinstance(param, ast.Parameter) and param.direction in (ast.PARAM_DIRECTION_OUT, ast.PARAM_DIRECTION_INOUT)): suffix = '*' else: suffix",
"* %s:\\n\" % (func.symbol, )) for param in func.parameters: self.out_c.write(\" * @%s\" %",
"ast.Function): with self._function(node): body = self._function_bodies.get... |
[
"super().__init__(prefix=\"tmp_clean_copy_\", dir=dir) self.copyname = Path(self.name) / Path(source).name shutil.copytree(str(source), str(self.copyname)) def __enter__(self) -> Path:",
"for line in iter(process.stdout.readline, b''): if not line: break outlines.append(line.decode().strip()) if self.verbose: pri... |
[
"paramiko ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) ssh.connect(hostname='172.16.58.3',username='ec2-user',password='<PASSWORD>',port=22) sftp_client=ssh.open_sftp() #sftp_client.get('/home/ec2-user/paramiko_download.txt','paramiko_downloaded_file.txt') #sftp_client.chdir(... |
[
"Call print_profile_data to print the collected data. :type func: Callable :rtype: Callable \"\"\"",
"except ImportError: from io import StringIO debug_on = False if debug_on: sublime.message_dialog(\"NimLime running",
"development & testing. \"\"\" from pprint import pprint import sublime try: from cProfile",
... |
[
"Mock() await self.monitor.setup() self.monitor.add_behaviour.assert_called_once_with( behaviour_mock() ) @freeze_time(\"1989-08-12\") @patch(\"driftage.monitor.FastNotifyContacts\") @patch(\"driftage.monitor.Template\") def test_should_notify_contacts_on_new_data( self, template_mock,",
") template_mock.assert_c... |
[
"import commands.todo as todo import commands.user as user app = fastarg.Fastarg(description=\"productivity app\", prog=\"todo\")",
"as user app = fastarg.Fastarg(description=\"productivity app\", prog=\"todo\") @app.command() def hello_world(name: str): \"\"\"hello world\"\"\"",
"str): \"\"\"hello world\"\"\" ... |
[
"declare_ok.queue, exchange, routing_key=\"test.5\", ) for i in range(10): messages = [ asyncio.ensure_future(channel.basic_publish( b\"test\",",
"future = loop.create_future() await channel.basic_consume( declare_ok.queue, future.set_result, no_ack=True, ) message = await future",
"await queue.get() assert mes... |
[] |
[
"def balanced_parens(str): ''' runtime: O(n) space : O(1) ''' if str is None:",
"for char in str: if char == '(': open_count += 1 elif char",
"O(1) ''' if str is None: return True open_count = 0 for char",
"elif char == ')': open_count -= 1 if open_count < 0: return False",
": O(1) ''' if str is None: retur... |
[
"* UpdateKey [REG_SZ], contains a date and time formatted as: \"MM/DD/YYYY hh:mm:ss [A|P]M\",",
"= (year, month, day_of_month, hours, minutes, seconds) try: date_time = dfdatetime_time_elements.TimeElements( time_elements_tuple=time_elements_tuple) date_time.is_local_time",
"= ' '.join(sorted(configuration)) or... |
[
"one inhabitant (according to eq and hash). \"\"\" @staticmethod def instance() -> 'Unit':",
"import Any, TypeVar X = TypeVar('X') class Closeable(metaclass=ABCMeta): @abstractmethod def close(self) -> None:",
"Exception('Absurd') @dataclass(frozen=True) class Unit: \"\"\" A simple type with one inhabitant (acc... |
[
"@pytest.fixture(scope=\"class\", autouse=True) def cube_default(self): cube_utils = CubeUtils() cube_utils.generate_cube(\"default_high_res\", 46, 2160, 4320) yield cube_utils",
"reading for large area spatial analysis high-res\", timer=time.perf_counter, disable_gc=True, warmup=False ) def test_read_default_hig... |
[
"'send_store_hours_flow') directives = convo.process('exit').directives assert_target_dialogue_state(convo, None) assert_reply(directives, templates=['Bye', 'Goodbye', 'Have a nice day.'])",
"returned by application templates (Union[str, Set[str]]): The reply must be a member of",
"of directives returned by app... |
[
"fig = plt.figure() rect = fig.add_subplot(111, aspect='equal') data0 = np.loadtxt('data0.dat', delimiter=',') data1 =",
"X[-1]) plt.ylim(Y[0], Y[-1]) plt.xlabel(\"\") plt.ylabel(\"\") plt.grid(ls='dotted') plt.savefig('{}.svg'.format(ID), bbox_inches='tight', pad_inches=0.1) plt.savefig('{}.eps'.format(ID), bbox... |
[
"Language from sarna.report_generator import make_run from sarna.report_generator.locale_choice import locale_choice from sarna.report_generator.style import RenderStyle",
"sarna.report_generator import make_run from sarna.report_generator.locale_choice import locale_choice from sarna.report_generator.style impor... |
[
"def _test_ap_open_wpas_in_bridge(dev, apdev): hapd = hostapd.add_ap(apdev[0]['ifname'], { \"ssid\": \"open\" }) br_ifname='sta-br0' ifname='wlan5' wpas",
"hostapd.add_ap(apdev[1]['ifname'], { \"ssid\": \"open\" }) def test_bssid_black_white_list(dev, apdev): \"\"\"BSSID black/white list\"\"\" hapd =",
"hostapd... |
[
"torch.nn as nn import torch.optim as optim class BinModel(nn.Module): expected_target_type = torch.FloatTensor def",
"= torch.from_numpy(transformed_y).type(class_model.expected_target_type) for e in range(n_epochs): train_epoch(class_model, class_opt, class_criterion, bin_t_X, bin_t_y) def train_regressor(X,",
... |
[
"* self.steps_per_day + 0.5 ).astype(int) if np.any(offset < 0) or np.any(offset > len(self._table)):",
"r[i] # as the fractional time during the night when the dome reopens.",
"A relative path name refers to the :meth:`configuration output path <desisurvey.config.Configuration.get_path>`. extra_downtime :",
... |
[
"Data Export API response into tabular data. \"\"\" __author__ = 'api.nickm@ (<NAME>)' import",
"the target stream self.stream.write(data) # empty queue self.queue.truncate(0) # pylint: disable=g-bad-name def writerows(self,",
"KIND, either express or implied. # See the License for the specific language",
"ou... |
[
"= tf.constant(dataset.values[window_size:], dtype=tf.float32) ds_label = tf.data.Dataset.from_tensor_slices(ds_label) ds_train = tf.data.Dataset.zip((ds_data, ds_label)).batch(128).repeat() return ds_train ds_train",
"30 column = 'high' epoches = 300 def batch_dataset(dataset): dataset_batched = dataset.batch(wi... |
[
"render_template('userprofile/index.html', userid=userid, form4=CommentForm(prefix='form4'), form3=editForm(prefix='form3'), form6=searchForm(prefix='form6'), form5=ReportForm(prefix='form5'),form7=AddPlaylist(prefix='form7'), friend=getFriend(userid), playlist=getPlaylist(userid), User=getUserData(userid), Comment... |
[
"tokenization from language.labs.drkit import input_fns import numpy as np import tensorflow.compat.v1 as tf",
"in predictions: if not skip_no_answer: message = \"Unanswered question %s will receive score",
"= result[\"qas_ids\"].decode(\"utf-8\") preds = result[\"top_idx\"] scores = result[\"top_vals\"] ans = ... |
[
"population to check our different SFRD/SED techniques sfrd_pars = {'pop_sfr_model': 'sfrd-func'} sfrd_pars['pop_sfrd'] =",
"test_pop_models.py Author: <NAME> Affiliation: UCLA Created on: Fri Jul 15 15:23:11 PDT 2016",
"pop_fcoll.src.Nion sed['pop_Nlw'] = pop_fcoll.src.Nlw # pop_Ex? sed['pop_ion_src_igm'] = Fa... |
[
"from leancloud._compat import to_bytes __author__ = 'asaka <<EMAIL>>' def sign_by_key(timestamp, key): return hashlib.md5(to_bytes('{0}{1}'.format(timestamp,",
"utf-8 import time import hashlib import leancloud from leancloud._compat import to_bytes __author__ =",
"time import hashlib import leancloud from lea... |
[
"initialize the simulation time sim_time = SIM.start_time # main simulation loop # sleep(5)",
"PUP, 2012 - Update history: 12/27/2018 - RWB 1/17/2019 - RWB \"\"\" import",
"print(\"Press Command-Q to exit...\") while sim_time < SIM.end_time: #-------set control surfaces------------- if(sim_time<25): delta_e",
... |
[
"pred_name, \"{} not in {}\".format(obj_name, pred_name) pred_path = osp.join(res_root, pred_name) assert osp.exists(pred_path), pred_path",
"\"04_05TomatoSoupCan/inference_model_final_wo_optim-5641f5d3/ycbv_005_tomato_soup_can_train_real_uw/results.pkl\", \"05_06MustardBottle/inference_model_final_wo_optim-6ce23... |
[
"'root': { 'authorities': ['role_admin'], 'password': '<PASSWORD>', } } def login(self, username: str, password:",
"class AccountService: accounts = { 'root': { 'authorities': ['role_admin'], 'password': '<PASSWORD>', } }",
"['role_admin'], 'password': '<PASSWORD>', } } def login(self, username: str, password: ... |
[
"print \"addends:\" print A print B print print \"sum: \" print A +",
"\"E\"] X = pd.Series([0, 1, 2], pd.Index(I2)) Y = pd.Series([10, 20, 0, 0],",
"\"addends:\" print X print Y print print \"sum:\" print X + Y print",
"twoDigit print print \"sum:\" print oneDigit + twoDigit print I2 = [\"A\", \"B\",",
"pr... |
[
"raise nelly.error('Please specify pre or post in code section') elif 'start_comment' == token:",
"parse zero or more expressions until a semicolon is found self._expressions('pipe', 'semicolon', nonterminal)",
"0 (token,value,line,col) = self.tokens.Next() if 'constant' != token: raise nelly.error('Missing ran... |
[
"\"\"\" Get the version of the currently installed QCoDeS \"\"\" return qcodes.version.__version__ def",
"Optional[bool] try: pipproc = subprocess.run(['python', '-m', 'pip', 'list', '-e', '--no-index', '--format=json'], check=True, stdout=subprocess.PIPE)",
"[] package_names = [list(requirements.parse(req))[0]... |
[
"== \"POST\": fieldname = request.POST['fieldname'] value = request.POST['value'] fc, created = FieldCategory.objects.get_or_create( fieldname=fieldname,",
"= request.POST['fieldname'] value = request.POST['value'] fc, created = FieldCategory.objects.get_or_create( fieldname=fieldname, value=value ) return",
"r... |
[
"be moved into a required_providers block. # def test_version_source(): # # import terrascript.provider.Mongey.kafka_connect",
"generated by tools/makecode.py (24-Sep-2021 15:20:11 UTC) def test_provider_import(): import terrascript.provider.Mongey.kafka_connect def test_resource_import(): from",
"to be moved i... |
[
"if 'token' not in login_session: return redirect('/login') user = session.query(User).filter_by(email=login_session['email']).first() categoryItem = session.query(Category).filter_by(name=category).first()",
".read())['web']['client_id'] APP_ID = json.loads(open('fb_client_secrets.json', 'r') .read())['web']['ap... |
[
"import NooException, cancel from .store import STORE, FileStore, Store __all__ = ( \"FileStore\",",
"echo, set_quiet from .errors import NooException, cancel from .store import STORE, FileStore, Store",
"from .errors import NooException, cancel from .store import STORE, FileStore, Store __all__ =",
".errors ... |
[
"np.logical_or(self.class_masks[cls], unique_masks[color_ind, ...]) def _image_depth(self, image_depth_data, **kwargs): image_depth = read_buffer_image(image_depth_data, self.screen_width, self.screen_height) depth_format",
"camera_near_plane = kwargs.pop('camera_near_plane', 0) if depth_format == DepthFormat.Nor... |
[
"description=\"\"\"A tool for extracting and converting Google-style docstrings to plain-text, markdown, and JSON.\"\"\",",
"tool for extracting and converting Google-style docstrings to plain-text, markdown, and JSON.\"\"\", url='http://github.com/ooreilly/mydocstring',",
"for extracting and converting Google-... |
[
"import struct from ..err import SerializeError from ..abc import * from ..core import",
"= {} kwargs.update(Options.pop_origin_kwargs(options)) self.check_options(options) try: return bson.dumps(obj, **kwargs) except Exception as e: raise",
"# -*- coding: utf-8 -*- # # Copyright (c) 2020~2999 - Cologler <<EMAI... |
[
"def\" def test_self_append_nonexistent_array(self): config = ConfigFactory.parse_string( \"\"\" x += [1,2] \"\"\" ) assert",
"assert config.get('t.e.y.h') == 'hey man' assert [v.strip() for v in config.get('t.e.y.i').split('\\n')] == ['',",
"config.get_string(\"A.field2\") == \"12\" assert config.get_string(\"... |
[
"None: self._agent.cleanup() self._agent = None CarlaDataProvider.cleanup() def load_scenario(self, scenario, agent=None): \"\"\" Load a",
"to analyze, this scenario has no criteria\") return True for criterion in self.scenario.get_criteria():",
"the ScenarioManager implementation. It must not be modified and i... |
[
"for obj in reversed(object_stack): assert obj is not None alter_cmd_cls = sd.ObjectCommandMeta.get_command_class_or_die( sd.AlterObject,",
"with context(sd.DeltaRootContext(schema=schema, op=delta)): schema = delta.apply(schema, context) return schema def derive_ref( self: ReferencedT,",
"Unless required by ap... |
[
"self.output += f\"{item['title'].replace(' ()', '')}, \" i += 1 if i >= n:",
"if i >= n: break return self.output[:-2] def processFilmography(self, list, n): self.output =",
"{month_name[int(elements[1])]} {elements[0]}\" def shortenText(self, string, n): #return first n sentences from string first",
"first ... |
[
"assign(service, arg): if service == \"shop7z\": return True, arg def audit(arg): payload =",
"curl.curl2(target) if code == 200 and 'name=\"lipinname\"' in res and 'name=\"showflag\"' in res:",
"def assign(service, arg): if service == \"shop7z\": return True, arg def audit(arg): payload",
"= 烽火戏诸侯 #_PlugName... |
[
"= \"lselect\" del command[\"on\"] elif flash == FLASH_SHORT: command[\"alert\"] = \"select\" del command[\"on\"]",
"supported_features self._rooms = rooms if is_group: self.is_osram = False self.is_philips = False self.is_innr",
"= \"none\" if ATTR_EFFECT in kwargs: effect = kwargs[ATTR_EFFECT] if effect == EF... |
[
"of :class:`~ezdxf.entities.LWPolyline` and 2D :class:`~ezdxf.entities.Polyline` entities start at the vertex which includes the",
"center of arc described by the given bulge parameters. Based on Bulge Center",
"compatible object bulge: bulge value as float \"\"\" start_point = Vec2(start_point) a =",
"2 a = ... |
[
"\"__main__\": # print \"Hello World\" from ProgrammingEmail import ManageAttachments import jpype import os.path",
"header, choose License Headers in Project Properties. # To change this template file,",
"template in the editor. #if __name__ == \"__main__\": # print \"Hello World\" from",
"editor. #if __name... |
[
"text = buffer.text cursor_position = buffer.cursor_position for m in reversed(list(WORD.finditer(text))): if m.start(0) <",
"event.current_buffer.document.selection: from_, to = event.current_buffer.document.selection_range() run_in_terminal(lambda:system_copy(event.current_buffer.document.text[from_:to + 1])) @... |
[
"loglikelihood import * from statistics import * from nested import * #import random",
"p530_p730_work=Beta('period 5:30pm to 7:30 pm in work',0,-10,10,0) p730_p1000_work=Beta('period 7:30pm to 10pm in work',0,-10,10,0)",
"driver_dummy_E*driver_dummy+\\ passenger_dummy_E*passenger_dummy+\\ public_dummy_E*public... |
[
"from flask_sqlalchemy import SQLAlchemy # 数据库操作对象 db = SQLAlchemy() # 创建app def create_app():",
"# 创建app def create_app(): # flask操作对象 app = Flask(__name__) # 通过配置文件读取并应用配置 app.config.from_object(Config) #",
"from app.view import employee # 职位管理子系统 from app.view import post # 部门管理子系统 from",
"# 初始化模块 from con... |
[
") def __open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def",
"__open_abi(self): return json.load(open(self._abi_path, \"r\")) @property def abi(self): return self.__json_abi @property def contract(self): return",
"def contract(self): retu... |
[
"fig1 = self.plot_log_dec(percentile, conf_interval, *args, **kwargs) subplots = make_subplots(rows=1, cols=2) for data in",
"is None: fig = go.Figure() default_values = dict(mode=\"lines\") conf_interval = np.sort(conf_interval) percentile =",
"p / 2, axis=0) p4 = np.percentile(self.yout[..., ndof * node + 1],... |
[
"for pair in itertools.combinations(STRATEGY_LIST, r=2): roundHistory = runRound(pair) scoresA, scoresB = tallyRoundScores(roundHistory) outputRoundResults(f,",
"range(LENGTH_OF_GAME): playerAmove, memoryA = moduleA.strategy(getVisibleHistory(history,0,turn),memoryA) playerBmove, memoryB = moduleB.strategy(getVis... |
[
"TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE",
"db: database to connect to within server :type db: string ''' # If",
"self.connection.cursor() # For if caller never exhausts the results by repeated calls: self.cursors.append(cursor)",
"BY THE COPYRIGHT HOLDERS AND CO... |
[
"FP:\", xkey.parent_fp.hex()) print(\"Child number:\", xkey.child_number_with_tick()) print(\"Chain code:\", xkey.chain_code.hex()) print(\"Key:\", xkey.key) if xkey.key.get_private_bytes(): print(\"Private",
"lib.utils import one_line_from_stdin xkey = XKey.from_xkey(one_line_from_stdin()) print(xkey) print(\"Ve... |
[
"of tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image",
"tangent image corners = tangent_image_corners(b, s) # Corners of each tangent image fov_x,",
"to examine def ang_fov(s): print('Spherical Resolution:', s) for b in range(s): dim =",
"spherical_distortion.util import * s... |
[
"raise RuntimeError(f\"Cannot find {name} in graph nodes. Graph: {self.graph}\") def __len__(self): #TODO: Update",
"attr)) for attr in ['start', 'stop', 'step']]) return node except Exception as ex:",
"self, graph=self.graph) if not _is_node_type_instance(other, (\"slice_op\", \"var_index\", \"index\")) else o... |
[
"models from django.utils.translation import ugettext_lazy as _ # Create your models here. class",
"name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to hollywood",
"Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be dire... |
[
"docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ == \"__main__\": main()",
"as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.ren... |
[
"0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection =",
"def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location =",
"self.pop() def rotation_matrix_2d(degree: float) -... |
[
"= get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values]))",
"\"between test times for each test are smaller than the threshold and the",
"urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SL... |
[
"\"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if",
"j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinit... |
[
"array to its original configuration and return it. \"\"\" return self.original def shuffle(self)",
"coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last",
"nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\"",
"Modified time: 2019... |
[
"- feature x column y - feature y column Returns ---------- phi coefficient",
"features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals,",
"clusters the features in X according to dist_matrix, and selects a feat... |
[
"\"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\",",
"= webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel<PASSWORD>\") # Google Search \"Selenium\" search.submit() elif(i... |
[
"system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the",
"the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configur... |
[
"override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), )",
"def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value',",
"cfgv.ValidationError: return False @p... |
[
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"for key in output_dict.keys(): if key in misc_keys: continue if key not in",
"output: continue output.on_option_tab_create(self.notebook) self.n... |
[
"f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='<NAME>', author_email='<EMAIL>', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0',",
"= f.read() ... |
[
"for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB",
"routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\... |
[
"new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip,",
"for the docker registry @param pwd the password for the docker registry ... |
[
"robot base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1.,",
"depth image :param depth_scale: depth scale that trans raw data to mm :return:",
"with the depth channel inp_image = pre_process(img_wo_at, depth) # pass the image into",
"(int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(k... |
[
"# kernel = np.array( # [[1, 1, 1], # [1, 5, 1], #",
"= 1.0): angle=level (h, w) = image.shape[:2] if center is None: center =",
"level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im =",
"= option_mode[mode] translate_pixel = int(width * level) M = np.... |
[
"REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules",
"__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName = None ## generate",
"Intel Corporation. All rights reserved.<BR> # # This program and the accompanying materials",
"THE PROGRAM IS DISTRIBUTED UNDER THE BSD L... |
[
"y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,",
"as file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #",
"Plot data fig, ax = plt.subplots() ax.plot(size, time)... |
[
"the request did not violate any quota. :type throttle_time_ms: int :param replica_election_results: The",
":type partition_id: int :param error_code: The result error, or zero if there was",
"throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The dura... |
[
"assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy =",
"log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if",
"stack.push(\"b\") the_copy = stack.copy() the_c... |
[
"AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds",
"class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_for... |
[] |
[
"for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def",
"(%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values =",
"text = valu... |
[
"self.src == self.dst: # The src=dst mode should be used if and only",
"else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs =",
"hashlib import logging import os import threading logger = logging.getLogger(__name__) ... |
[
"raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task (by",
"return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install')",
"metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments m... |